Introduction
In 2021, Netflix published its first diversity report in which it listed the cast of all US-commissioned films and series released in 2018 and 2019. In this report, they showed that, overall, Netflix is well ahead of other entertainment outlets regarding the inclusive representation of minority groups (Sarandos, 2021). This report is an indication of a growing commitment to diversity and inclusion in the world of entertainment media, which was also reflected in, for example, a boost in Chief Diversity Officers in Hollywood after 2016 #OscarsSoWhite (Sun, 2022). Additionally, this trend is also not solely an American endeavor. For example, in the Netherlands and Great Britain, there was also a boost in commitments to diversity and inclusion in hiring and casting practices in both public and commercial television programming (DiversityUK, 2020; NPO, 2023; SER, 2021).
These pledges of inclusion of minority groups in mediated entertainment are very interesting considering decades of research highlighting the underrepresentation and stereotyped representation of minority groups in entertainment media (Asmar et al. 2023; Daalmans and ter Horst, 2017; GLAAD, 2023; Sarandos, 2021; Wiart, 2022). And even with the growing amount of time spent on social media, television is still a significant socializing agent in contemporary society, through its engagement of viewers for several hours a day (Insider Intelligence, 2022; SKO, 2022). Television viewing has changed tremendously over the last decade with the introduction of and advancements in online video streaming platforms such as Netflix, Amazon, and Disney+. These streaming platforms are intensely popular worldwide and form an addition to more traditional TV viewing possibilities such as public or commercial broadcasters (Blomeley, 2021; Demont-Heinrich, 2022).
Over the past decades, scholars have conducted research that has attempted to understand the relationship between television audiences and the consequences of the mediated content they consume. Prime-time television research has specifically contributed immensely to the insight into media representations and the inherent consequences for their audiences, in terms of their views of themselves, others, and the world at large (Morgan et al., 2015). More specifically, previous content analytic work has outlined how minority groups have been represented in terms of presence (compared to their presence in society, i.e., recognition) and type of representation (in terms of stereotyped presentation, i.e., respect) (Clark, 1972; Lauzen and Dozier, 2005; Signorielli and Bacue, 1999). These studies have outlined patterns of underrepresentation as well as stereotyped presentation of minority groups, such as women, ethnic minorities, the elderly, and sexual minorities.
The breadth of the literature has largely focused on prime-time programming, television commercials, and specific television genres such as situation comedies (Battles and Hilton-Morrow, 2002; Daalmans et al., 2017; Furnham and Lay, 2019; Lauzen and Dozier, 2005; Mastro and Greenberg, 2000; Signorielli and Bacue, 1999). Nevertheless, the television landscape has undergone significant changes over the past decade, with the advent and popularity of streaming platforms across the world. And while some research has focused on Netflix’s representation of minorities, particularly in film and scripted series (Bucciferro, 2019; González-de-Garay et al., 2023; Ramos and Gonzalez-De-Garay, 2021; Pietaryte and Suzina, 2023; Ramón and Hunt, 2019; Smith et al., 2021), other popular streaming platforms remain understudied. This means that no systematic study has analyzed yet whether recent commitments to diversity and inclusion have shown rewards in popular programming and if potential differences are present between more traditional broadcasters (public and commercial broadcasters) and popular streaming platforms. This gap in the literature will be addressed in the current study in which we aim to analyze the representation of minority groups, through the lens of recognition and respect (Lauzen and Dozier, 2005; Signorielli and Bacue, 1999), on Dutch prime-time television as well as the most popular streaming platforms in the Netherlands. As such we aim to answer the research questions:
RQ1: How are minorities represented in terms of recognition and respect on Dutch television?
RQ2: Are there differences in this representation, in terms of recognition and respect, based on the type of platform (regular broadcasting vs. streaming)?
Theoretical background
Based on the significant amount of time that we spend with television on a daily basis, a demonstrable impact on how the public at large perceives the world and how individuals create an image of themselves and of others is often theorized (Cottle, 2000; Morgan et al., 2015). Social learning theory is often applied since it posits that people can learn behaviors and norms by watching other people conduct themselves in a certain manner (Bandura, 1977). These people are called models and can be real-life persons, but the theory has also been extended to mediated models.
Cultivation theory articulates that media are our primary storytellers and, as such, a significant institution in the socialization processes of our culture (Gerbner, 1998). One prominent way in which television works as a socializing agent is by conveying and signifying information about certain (minority) groups to viewers and users, thereby impacting the ways in which we see ourselves and others.
Both social learning theory and cultivation theory are used to stress the importance of representational research (Daalmans et al., 2017, Signorielli, 2009), indicating that if we learn about the world, ourselves, and others through mediated representations, it is highly important that we know what we are exposed to in whom we see, and what they do. Therefore, the current study builds on the central ideas of social learning and cultivation research, in that people learn behaviors and attitudes (regarding gender, ethnicity, age, sexual orientation etc.) from portrayals on television, and these representations then function as a significant source of information to create meanings about themselves and others (Lauzen and Dozier, 1999). As such, we believe it is of vital importance to know what these televised representations are “showing and telling” to their viewers, which is why empirical representational research such as the current work is a necessary foundation for further future explorations about the impact of representation.
The representation of minorities has been predominantly analyzed through the previously mentioned concepts of recognition and respect (e.g., Daalmans et al., 2017; Gerding and Signorielli, 2014; Lauzen and Dozier, 2005; Signorielli and Bacue, 1999). These concepts were originally constructed by Clark (1972) in analyzing the process of legitimation of African Americans via media content, and the subsequent consequences for Black viewers. Clark’s work puts forth the idea that when television fails to show characters of a particular social group (i.e., women, ethnic minorities, the elderly, and sexual minorities) that social group is denied recognition. Additionally, when television representations show them negatively or stereotypically, that social group is being denied respect. Because of its time-consuming role in society, television functions as a primary source of information about social groups (with which we might have limited contact in real life). Without recognition and respect in these forms, social groups are more likely to be devalued by society and stereotypical thoughts about these groups will persist (Daalmans et al., 2017; Signorielli and Bacue, 1999). Previous research on the representation of minority groups has revealed that it remains lacking for minority groups, in general, both in terms of recognition as well as respect.
In the current study, we aim to update this knowledge of representation for minority groups and extend it by comparing this representation between television platforms (regular broadcasting vs. streaming). In the following sections, previous studies regarding the representation of minority groups in television content will be discussed through the lens of recognition and respect and used to posit expectations regarding different minority groups (specifically gender, ethnicity, age, and sexual orientation). At the end, expectations regarding the representation on different television platforms are formulated.
Gender
Previous studies into gender representation have found that women remain underrepresented—and therefore lacking in recognition—across television programs compared to their presence in society (Daalmans and ter Horst, 2017; Emons et al., 2010; Glascock, 2001; Lauzen, 2022; Lynch et al., 2016; Sink and Mastro, 2017; Smith et al., 2010). Even though some changes have been made over the past decades, and some small steps have been taken toward a more equal distribution of male and female characters, these increases are often minor, varying in proportions, and structurally, women remain underrepresented (Daalmans and ter Horst, 2017; Emons et al., 2010; See Jane, 2021; Segijn et al., 2014; Signorielli and Bacue, 1999). Besides the lack of recognition for women, the way men and women are represented significantly differs when looking at the portrayal of their social roles on screen (Lynch et al., 2016; Signorielli and Bacue, 1999; Sink and Mastro, 2017). That is, the amount of respect given to them is different from that of men.
While respect is sometimes conceptualized in slightly different manners in various previous research, cumulatively, these studies provide compelling and convincing trends in respect to this regard. Starting at the fulfillment of social roles, women are overrepresented (compared to men) in items on interpersonal contact in the private sphere (e.g., family and romance) (Cann and Mohr, 2001; Emons et al., 2010; Daalmans and ter Horst, 2017; Pennekamp, 2011; Segijn et al., 2014). In this regard, men are more than women often shown in working roles (Daalmans et al., 2017; Downs and Smith, 2010; Sink and Mastro, 2017; Smith et al., 2013). Conversely, women are more often than men seen in roles wherein they are represented in familial and caretaking roles, wherein their romantic and parental status is made more explicit than for men (Daalmans et al., 2017; Davis, 1990; Emons et al., 2010; Glascock, 2001; McNeil, 1975). Overall, women are shown less respect in their representation than men.
With regard to gender, we thus expect:
H1:Women are underrepresented on Dutch television compared to their proportions in Dutch society.
H2: Women are represented a) more than men in themes related to family and b) interpersonal relations, c) less than men in working roles, and more than men as d) romantic partners, e) parents and in f) caretaking roles.
Ethnicity
Previous research has shown that the representation of ethnic minorities also does not reflect reality (Koeman et al., 2007; Mastro and Greenberg, 2000; Shohat and Stam, 2014; Segijn et al., 2014). Even though ethnic minorities form a substantial group in all societies (including Dutch society), they have been historically underrepresented in mediated representations (Koeman et al., 2007). The degree of underrepresentation does vary between different ethnic minorities since, for example, Latinos, Asian Americans, and Native Americans are continuously underrepresented, while the proportion of Black people in mediated entertainment has been documented to be more reflective of proportions in society (Mastro, 2009; Tukachinsky et al., 2015). However, this parity for Black people in television has also been documented to be part of very specific programming (i.e., comedy shows), rather than in television programming as a whole (Signorielli, 2009). Overall, over time, some strides toward more representative representation have been documented in research (Daalmans and ter Horst, 2017). This means that some steps towards the recognition of ethnic minorities are reflected in research. Nevertheless, these steps toward recognition do not befall all ethnic groups equally.
In terms of respect, steps towards more equal representation are less prevalent. When ethnic minorities are represented, they are often stereotypically portrayed through, for example, negative portrayals in terms of attractiveness, story function, and personality traits (Chiricos and Eschholz, 2002; Eschholz et al., 2004; Mastro and Greenberg, 2000; Mastro and Robinson, 2000; Segijn et al., 2014). Furthermore, ethnic minorities have been historically and systematically overrepresented within the theme of criminality, both as victims and perpetrators of crime (Chiricos and Eschholz, 2002; Chory-Assad and Huang, 2000; Daalmans and ter Horst, 2017; Eschholz et al., 2004; Mastro and Greenberg, 2000; Oliver, 1994).
We, thus, predict:
H3: Ethnic minorities are underrepresented on Dutch television compared to their proportions in Dutch society.
H4: Ethnic minorities are represented more than Caucasians a) in themes related to criminality, and b) as both perpetrators and victims of crime.
Age
Another minority group that has been studied extensively over the past decades are seniors. Different content analyses from around the world found that older people are less present on television programs than in society (Daalmans and ter Horst, 2017; Kessler et al., 2004; Signorielli, 2001, 2004). Several studies found that characters over the age of 65 tend to drastically disappear from the screen, making up for only 2.8% to 3.3% of all characters (Lauzen and Dozier, 2005; Robinson and Skill, 1995; Signorielli, 2004; Vernon et al., 1991).
Research shows that older people are, besides being underrepresented on television, also represented in several fixed ways and, therefore, also receive less respect than other age groups do (Daalmans and ter Horst, 2017; Gott, 2005; Lepianka, 2015; Luther et al., 2018; Reul et al., 2022; Van Gorp, 2013). Their representation tends to confirm mostly negative cultural myths about older people and aging (Daalmans and ter Horst, 2017), which emphasize vulnerability and decline (Reul et al., 2022). Older people on television are, therefore, often associated with themes related to health (issues) (Van Gorp, 2013; Luther et al., 2018), and they are often portrayed as forgetful and dependent (Koeman et al., 2007; Smythe, 2003). Besides the overrepresentation concerning health, illness, and vulnerability, older people are less engaged in (sexual) intimacy on screen (Gott, 2005), while the elderly are in reality living longer, living healthier and are more often engaging in a variety of intimate and sexual behaviors (Lindau and Gavrilova, 2010; Syme, 2014). However, research reveals this is not represented in television as such.
This leads to the expectations that:
H5: The elderly are underrepresented on Dutch television compared to their proportions in Dutch society.
H6: The elderly are represented a) more than other age groups in themes related to health, and b) less often than other age groups physically intimate.
Sexuality
Heterosexual persons have been historically systematically overrepresented on television (Bond, 2014; Daalmans and ter Horst, 2017; González-de-Garay et al. 2023), while sexual minorities have historically been underrepresented on television and, therefore, lacked recognition (Gross, 1989). However, while most reports still reveal underrepresentations when compared to societal proportions, there are positive developments toward greater inclusion of LGBTQIA+ characters in television programming (GLAAD, 2021; Kerrigan and Vanlee, 2022; Vanlee et al., 2018).
Problematic in gauging the categorization of sexual orientation, within most content analytic research, is that it is an unknown category for most people represented on television since there needs to be an explicit manifestation or expression of sexuality to be able to code this information (Daalmans and ter Horst, 2017; González-de-Garay et al., 2020; Pennekamp, 2011; Raley and Lucas, 2006). For example, in a study by González-de-Garay et al. (2020), they reported 47.0% of the coded characters to be heterosexual, only 1.0% to be homosexual, and 0.1% as having another sexual orientation. But 51.9% of the characters, they were unable to code sexual orientation since this was not explicated or shown in the program. This has complicated comparisons of the actual presence of sexual orientation in society, with representations of sexual orientation. Nevertheless, research does consistently reveal that in terms of recognition, when considering the persons whose sexual orientation is known, sexual minorities are underrepresented compared with their proportion in society in prime-time television.
While there have been some positive developments reported in terms of recognition, this is less clear for respect in that the representation of sexual minorities is (still) often stereotyped. For example, through a focus solely on themes in the private sphere, such as romance and family (Battles and Hilton-Morrow, 2002; Fejes and Petrich, 1993; Streitmatter, 2009) or solely as a source of comic relief in programs (Fouts and Inch, 2005). Compared to heterosexuals, sexual minorities are also often portrayed as sexless. This means that compared to heterosexuals’ sexual minorities will engage in physical intimacy less frequently on television (Bond, 2014; Bond et al., 2019; Raley and Lucas, 2006). Finally, even though the representation of LGBTQIA+ persons has thankfully outgrown its period of casting sexual minorities as sick, dying, or abnormal (Gross, 1989; Hart, 2000; Herman, 2005; Shugart, 2003), research reveals that sexual minorities are still often represented as victims of (sexualized) crime (Hurd et al., 2020; Morrison et al., 2021).
Taken together this leads to the following expectations:
H7: Sexual minorities are underrepresented on Dutch television compared to their proportions in Dutch society.
H8: Sexual minorities are represented a) more than heterosexuals in themes related to interpersonal relationships, b) are less often than heterosexuals shown as physically intimate, and c) are more often than heterosexuals portrayed as a victims of crime.
Differences between types of platforms
As outlined in the introduction, streaming platforms have been committed to diversity and inclusion in their programming, for example, in the presence of Chief Diversity (and Inclusion) Officers. This is, in part, a moral commitment to inclusion and diversity in their content, but additionally and even more prominently, it serves the financial purpose of appealing to a diverse group of customers with their content. A 2020 Nielsen report reveals that streaming platforms outperform both cable and broadcast platforms in terms of diverse representations.
For gender, some research has already stated that Netflix is committed to more equitable gender representations (Bucciferro, 2019; Smith et al. 2021), although other studies argue that they show similar patterns of underrepresentation that have been previously reported (González-de-Garay et al., 2023; Ramos and Gonzalez-De-Garay, 2021), and therefore do not outperform other broadcasters in this regard (Pietaryte and Suzina, 2023). With regard to ethnicity, a comparative study conducted by Ramón and Hunt (2019) reveals that regular broadcasters, cable broadcasters, and video-on-demand platforms underrepresent ethnic minorities as lead characters compared to societal proportions, with cable broadcasters showing slightly higher percentages of minority lead characters. Additionally, research that has been conducted about specifically Netflix’s inclusion of ethnic diversity, does not reflect its ambitions as listed by Netflix’s own inclusion report (Sarandos, 2021), revealing that Netflix’s U.S. scripted series and films showcase an underrepresentation of various ethnic groups (Smith et al., 2021). However, the results of this study also showed that the percentages of Black people in films and series were quite representative compared to the US population, while for other ethnicities, in line with prime-time programming studies (Mastro, 2009; Tukachinsky et al., 2015), underrepresentations remains a fact. For age, there has been no study thus far analyzing the presence of the elderly on streaming platforms, but based on the attention to diversity and inclusion, it is expected that streaming platforms will present a more diverse representation in this regard than broadcast platforms. Finally, for sexuality, there are no indications yet that streaming platforms proportionally represent sexual minorities (González-de-Garay et al., 2023; Smith et al., 2021). However, some research does report higher proportions of sexual minorities being present on streaming platforms (Ramos and Gonzalez-De-Garay, 2021) than on regular broadcasting platforms.
Furthermore, a large difference between the Dutch NPO, the public broadcasting services funded by the government, and commercial broadcasters and streaming platforms, is that the public broadcasting services are bound to measures of diversity and inclusion by law. Under the terms of the Concessionary Act of 2000 and the Media Act of 2008, the Dutch public broadcasting system should address all groups of society and (re)present them in the most balanced fashion possible (Daalmans and ter Horst, 2017). However, even though these policies are in place and moving toward incorporating Chief Diversity officers seems to show dedication to this cause, the “question remains in how far the audience-oriented programming policy of the public-service system is effectively more successful than the consumer-oriented approach of the commercial stations in reflecting the diversity of Dutch society” (Koeman et al., 2007). Studies comparing public and commercial broadcasters have so far shown that in terms of underrepresentation (which is present in both), public broadcasters sometimes outperform commercial broadcasters (e.g., gender and ethnicity) and vice versa (e.g., age) (Daalmans and ter Horst, 2017; Pennekamp, 2011).
All in all, although the scant research that has focused on the representation of minorities on streaming platforms reveals mixed outcomes in terms of underrepresentation (Smith et al., 2021), and even though some Dutch broadcast agencies have moved to incorporate a Chief Diversity Officer in their organization, we expect—in line with the Nielsen report (2020) and previous research on diversity in legacy broadcasters (PBS and commercial)—we still expect that streaming platforms are a step further in more representatively and equally representing minorities in prime-time television. We thus predict:
H9: Streaming platforms will be more inclusive regarding a) gender, b) ethnicity, c) age, and d) sexuality in terms of recognition and respect than regular Dutch broadcasting platforms (both public service and commercial).
Method
To test the hypotheses as well as answering our overarching research questions, we conducted a quantitative content analysis based on a sample of Dutch prime-time television programs from public and commercial channels as well as popular programming on the most popular streaming platforms in 2022 (N = 142). The study was preregistered on AsPredicted1, and the coding book, an overview of the complete sample and the dataset can be found on OSF2.
In this study, we included four groups related to recognition (i.e., gender, ethnicity, age, sexuality) and several categories/behaviors that can be seen as stereotypical and thus as an indication of a lack of respect for at least one of these groups (e.g., the theme of the content in which they appear, parental status, occupation). In this article, we present the results for all the hypotheses about the recognition of minority groups (H1, H3, H5, H7), for the indicators of respect where we had specific predictions regarding a particular group (H2, H4, H6, H8), and the differences in these regards between regular broadcasting and streaming platforms (H9).
Sample
The Dutch landscape of audiovisual media services includes legacy broadcasters (e.g., public broadcaster NPO and commercial television operators organized within larger holdings, specifically RTL GROUP and TALPA Network), domestic on-demand services (e.g., NPO Start and Videoland) and multinational streaming services (e.g., Netflix and Disney+)(Idiz et al. 2021). Reports on media consumption patterns showcase that most Dutch people still watch linear content in the evening—that is, during prime time—and that streaming platforms are currently outperforming regular broadcasters (both public and commercial) in hours of viewing per day (Redactie Trouw, 2024; SKO, 2022; Schaper, Wennekers and de Haan, 2019). In terms of popularity, Netflix has the largest share of subscribers, followed by Videoland, Disney+, and Amazon Prime (Redactie Trouw, 2024).
Since this study deals with the most popular content of the most popular streaming platforms in the Netherlands, and the most popular time frame in broadcast television (prime time 19:00–23:00), we had to create a constructed sample. For the streaming platforms, we chose the most popular platforms in the Netherlands (Netflix, Amazon Prime, Disney and Videoland). We created our sample on January 10th, 2022, and chose the top 10 programs for that day for Netflix and Amazon Prime, the first 10 programs on the trending list for Disney, and the first 10 programs in the “now popular” list for Videoland (similar to sampling strategy employed by Sadza et al., 2023). This then resulted in ten programs per streaming platform selected on that day. For broadcast television, we discern between commercials and public broadcasting channels. They broadcast on average 4 to 5 programs in the prime-time period (19:00–23:00) per channel. To match the number of programs selected per streaming platform with broadcast television we chose to create a constructed sample in which every regular broadcaster was included in two nights (between January 10th and January 31st). The final constructed sample consists of N = 142 programs from the broadcasters and streaming platforms combined.
In terms of build-up, 44.2% of the sample was programming from The Netherlands, 47.1% was programming from the United States, 6% was programming from Great Britain, and 2.7% of the sample programs were created in another country. Broadly looking at genre distinctions (similar to Daalmans and ter Horst, 2017), News and Information programming was 22.7% of the sample, Reality and Entertainment programming was 22.0% of the sample, and Fictional content was 55.3% of the sample.
Coding procedures3
To explore the demographic aspects of the cast of prime-time programming, the programs were coded on a general level of information and on the level of the cast. The coding instrument used to analyze the persons and characters in the sample (see Table 1 for coding definitions, categories, and frequencies) was developed using prior studies of prime-time television (Bond et al. 2019; Commissariaat voor de Media, 2021; Daalmans, Kleemans, et al. 2017; Daalmans and ter Horst, 2017; Dillman Carpentier et al. 2017; Elasmar et al. 1999; Fisher et al. 2004; Koeman et al. 2007; Lauzen and Dozier, 2005; Manganello et al. 2008; Oliver, 1994; Pennekamp, 2011; Segijn et al. 2014; Signorielli and Bacue, 1999; Signorielli and Bievenour, 2015).
Table 1. Overview of coded variables for this study.
Variable | Definitiona | Categories | Frequencies n (%) | Kalpha |
|---|---|---|---|---|
Channel | What channel is the program broadcast on? (N = 142) | Public NL 1, 2, and 3, or | 34 (24%) | 1.00 |
Commercial - RTL 4, 5, 7 and 8, Net5, Veronica and SBS6 or | 68 (48%) | |||
Streaming – Netflix, Amazon Prime, Disney+ or Videoland | 40 (28%) | |||
Gender | Person’s gender (N = 2531) | - Male | 1158 (62.7%) | 1.00 |
- Female | 941 (37.2%) | |||
- Other (Trans, Non-Binary, Inter-sex) | 2 (0.1%) | |||
- Unknown | 2 (0.1%) | |||
Ethnicity | What is the person’s ethnicityb? (N = 2531) | - White (Western-European, Eastern-European, Extra-European (United States, Australia, …) | 1969 (77.8%) | 0.97 |
- Black (African, Afro-American, Central-American (Jamaica, Suriname) | 234 (9.2%) | |||
- Asian (Chinese, Japanese, Korean, Vietnamese, Indonesian) | 60 (2.4%) | |||
- South Asian (India / Pakistani) | 36 (1.4%) | |||
- South American (Latin, Hispanic) | 67 (2.6%) | |||
- Mediterranean Southern Europe (Spanish, Italian, Greek, Portuguese) | 9 (0.4%) | |||
- Mediterranean Maghreb / Arabic (Moroccan, Tunisian, Algerian, Libyan, Turkish, Syrian, Lebanese, Egyptian, …) | 97 (3.8%) | |||
- Other ethnicities (Arctic / Native American / Aborigine / Maori / …) | 4 (0.2%) | |||
- Ethnicity unclear, but not white (mixed-race, mulat ….) | 44 (1.7%) | |||
- Unknown | 11 (0.4%) | |||
Age | Person’s age in terms of the life cycle (N = 2531) | - The person is a child (0–12 years old) | 70 (2.8%) | 0.98 |
- The person is a teenager (13–18 years old) | 77 (3.0%) | |||
- The person is a young adult (19–35 years old) | 753 (29.8%) | |||
- The person is an adult (36–64 years old) | 1482 (58.6%) | |||
- The person is a senior (65 years and older) | 143 (5.6%) | |||
- Unknown / Unable to code | 6 (0.2%) | |||
Sexual orientation | What is the person’s sexual orientation? (N = 2531) | - Heterosexual | 521 (20.6%) | 0.99 |
- Homosexual (including Lesbian) | 35 (1.4%) | |||
- Bisexual | 1 (<0.01%) | |||
- Other (e.g., asexual, pansexual and demisexual) | 1 (<0.01%) | |||
- Unknown | 1973 (78.0%) | |||
Theme | What is the thematic subject that is central for the person that you are coding in the program? (N = 2418) | - Family | 262 (10.8%) | 0.99 |
- Crime, justice, police and safety | 602 (23.8%) | |||
- Politics (national and international) and state relations | 142 (5.6%) | |||
- Health(care) | 183 (7.2%) | |||
- Education and science | 47 (1.9%) | |||
- Culture, art, media and entertainment | 247 (9.8%) | |||
- Economy, finance and corporate life | 329 (13.0%) | |||
- Friendship, romance and leisure time (i.e., interpersonal relations) | 476 (18.8%) | |||
- Sports | 40 (1.6%) | |||
- Other/unknown | 90 (3.7%) | |||
Relationship | Is the person shown in a romantic relationship or is the relationship mentioned in the program? (N = 2418) | - Yes | 314 (13.0%) | 1.00 |
- No | 184 (7.6%) | |||
- Unknown | 1920 (79.4%) | |||
Parenthood | Is the person shown in a parental role or is the fact that the person is a parent mentioned in the program (including pregnancy) (N = 2418) | - Yes | 272 (11.2%) | 1.00 |
- No | 2146 (88.8%) | |||
Physical intimacy | Is this person visually (implied) physically intimate with themselves or other persons? (N = 2418) | - Yes, arm around shoulder | 89 (3.7%) | 0.97 |
- Yes, hugging, cuddling and kissing cheek | 160 (6.6%) | |||
- Yes, kissing, petting and touching over clothing | 67 (2.8%) | |||
- Yes, sexual acts | 65 (2.7%) | |||
- No | 2032 (84.0%) | |||
- Unknown / Unable to code | 5 (0.2%) | |||
Occupation | Does the person have an occupation? (N = 2418) | - Yes | 1358 (56.2%) | 0.98 |
- No | 120 (5.0%) | |||
- Unknown | 940 (38.9%) | |||
Household and caregiving tasks | Was the person or character engaged in household or caregiving tasks (for example: cleaning, doing the laundry, or taking children to school)? (N = 2418) | - Yes | 161 (6.7%) | 1.00 |
- No | 2257 (93.3%) | |||
Involvement in crime/law | Is the person involved with crime, criminality, or law enforcement? (N = 2418) | - Yes, as a perpetrator | 156 (6.5%) | 0.99 |
- Yes, as an accomplice | 32 (1.3%) | |||
- Yes, as a bystander | 45 (1.9%) | |||
- Yes, as a victim | 117 (4.8%) | |||
- Yes, as someone involved in law enforcement | 319 (13.2%) | |||
- No | 1749 (72.3%) |
aFor all the categories, the characteristic had to be visibly shown, mentioned or implied.
bTo determine ethnicity of a person, color and context were taken into consideration. The focus in coding was on the information given in the program (i.e., subtitles or spoken words). If this information was not present or unclear, then visible characteristics such as skin color, facial features, hair type and hair color, and shape of eyes were considered. If the person that was coded did not uniformly fall into one of the categories, but they could not be coded as white then they were coded as ethnicity unclear, or if there was doubt then they were coded as Unknown.
Coder training and reliability
Coding was divided over four coders. Coders practiced applying the coding scheme with materials that were not part of the sample and came from various platforms. They independently coded the material and then met together to discuss coding discrepancies, under supervision of the primary researcher. After these discussions, the coding instrument was edited to fix potential problems before coding and analysis. This was done in several sessions. When there were no more discrepancies and when final revisions were made, the coders individually coded around twenty percent (n = 30) of the programs in the sample. These programs were randomly selected across the coders and double-coded for reliability checks. Coders consulted the primary researcher when there was disagreement, which was then resolved by the researcher. Based on this overlap the levels of inter-coder reliability were calculated using Krippendorff’s alpha (using macro by Hayes; Hayes & Krippendorff, 2007) for all variables used in the analysis. The reliability for each of the variables can be found in Table 1. As the reliability scores were good, the remaining part of the sample was divided over the coders to code individually.
Analysis procedure
Data were analyzed using statistical analysis software. To determine the results, we used cross-tabulations with the Chi-square test and indicated where adjusted standardized points to over- or underrepresentation, as determined by the expected and observed frequencies.
Results
Our sample contained 2531 persons, of which we coded gender, ethnicity, and age. For sexual orientation, we could only code a subsample of 558 persons (for all the other persons in the sample, sexual orientation was not mentioned or shown, and therefore, it was coded as unknown). It is important to note that for the analyses, we reduced the complexity of the variables to only two or three levels (e.g., male-female; Caucasian-ethnic minority, <18 years old, 19–64 years old and 65 and older; heterosexual orientation-non-heterosexual orientation, excluding other categories or unknown categories from the analyses.
Recognition
Our data about the television cast was compared with and tested against real-life proportions of minority groups in Dutch society in 2022, based on data from the Dutch census office (Centraal Bureau voor de Statistiek, 2022) or The Netherlands Institute for Social Research (Huijnk, 2022).
As shown in Table 2, women make up a slightly larger share of society than men in Dutch society, while men are significantly overrepresented on all platforms of Dutch television (H1 supported). For ethnic minorities, the results revealed a significant overrepresentation of ethnic minorities compared to their proportion in society (H3 not supported). As expected, the elderly lack recognition on Dutch television since they are significantly underrepresented on Dutch prime-time television and popular streaming platforms compared to their proportion in Dutch society (H5 supported). Finally, the data revealed that the proportion of sexual minorities on television does not significantly differ from reality (H7 not supported).
Table 2. Presence of minorities on television compared with census data.
Overall | Census (CBS) | ||
|---|---|---|---|
Gendera | χ2 (1) = 172.033, p < 0.001 | ||
- Male | 62.8% | 49.7% | |
- Female | 37.2% | 50.3% | |
n | 2527 | 17,590,672 | |
Ethnicityb | χ2 (1) = 63.273, p < 0.001 | ||
- White/Caucasian | 78.5% | 85.2% | |
- Ethnic minority | 20.5% | 14.8% | |
n | 2476 | 17,590,672 | |
Agec | χ2 (1) = 325.744, p < 0.001 | ||
- Young (<18) | 5.8% | 21.2% | |
- Adult (19–64) | 88.5% | 58.7% | |
- Senior (65+) | 5.7% | 20.1% | |
n | 2525 | 17,590,672 | |
Sexual orientationd | χ2 (1) = 1.112, p = 0.290 | ||
- Heterosexual | 93.4% | 94.4% | |
- Sexual minority | 6.6% | 5.6% | |
n | 558 | 17,590,672 |
aChi-square test was calculated based on a comparison of TV cast (per platform) and census data (CBS, 2022) in which gender was reduced to a binary category, excluding categories of other and unknown.
bEthnicity was calculated based on a comparison of TV cast per platform with CBS 2022 data of Caucasian and non-western allochthonous proportions. TV data categories of unclear or unknown were not used in the analysis.
cChi-square was calculated based on a comparison of TV cast (senior/non-senior) with CBS 2022 data of non-senior (<65) and senior (>65), excluding categories of other and unknown.
dReal-life proportions of sexual minority orientation (5.6% LHBT) were based on SCP data (Huijnk, 2022); sexual orientation of TV cast is a subsample which reduced to either sexual minority or heterosexual, excluding category unknown.
Respect
In terms of respect, as can be seen in Table 3, our results showed that women are significantly overrepresented in themes related to family and interpersonal relations. Furthermore, the results revealed a significant overrepresentation of working men (60%) and women whose working status is unknown (44.6%). Overall, women are more often the men shown as romantic partners (16.6% compared to 10.8% for men), as parents (15% vs 9%), and in household and caregiving tasks (10.2% vs 4.6%). In terms of respect, this means that, overall, women are granted less respect on television than men (H2 supported).
Table 3. Gender representation in terms of respect.
Men | Women | ||
|---|---|---|---|
Theme | χ2 (10) = 111.598, p < 0.001 | ||
- Family | 8.0% | 15.7%a | |
- Justice, police, and safety | 29.0%a | 18.0% | |
- Politics | 6.5% | 4.9% | |
- Health(care) | 5.7% | 10.6%a | |
- Education and science | 1.9% | 2.0% | |
- Culture, art, media, and entertainment | 9.2% | 12.0%a | |
- Economy, finance, and business | 15.8%a | 10.0% | |
- Friendship, romance, and leisure time | 17.8% | 22.8%a | |
- Sports | 2.1%a | 1.0% | |
- Other | 3.2% | 2.1% | |
- Unknown | 0.9% | 0.9% | |
n | 1508 | 906 | |
Relationship | χ2 (2) = 18.108, p < 0.001 | ||
- Yes | 10.8% | 16.6%a | |
- No | 7.3% | 8.2% | |
- Unknown | 81.9%a | 75.3% | |
n | 1508 | 906 | |
Parental status | χ2 (1) = 20.328, p < 0.001 | ||
- Yes | 9.0% | 15.0%a | |
- No | 91.0%a | 85.0% | |
n | 1508 | 906 | |
Occupation | χ2 (2) = 28.193, p < 0.001 | ||
- Yes | 60.3%a | 49.3% | |
- No | 4.3% | 6.1% | |
- Unknown | 35.3% | 44.6%a | |
n | 1508 | 906 | |
Household & caregiving tasks | χ2 (1) = 28.300, p < 0.001 | ||
- Yes | 4.6% | 10.2%a | |
- No | 95.4%a | 89.8% | |
n | 1508 | 906 |
aFrequency significantly exceeded expectations by adjusted standardized residuals.
As can be seen in Table 4, contrary to expectation, ethnic minorities are not significantly overrepresented in themes related to criminality (H4a not supported). Even though this relationship was not found, the results did reveal ethnic differences in the presence of criminal actors. In line with our hypothesis, the results revealed that, overall, ethnic minorities are significantly overrepresented as perpetrators (8.5% vs 5.5%) and victims of crime (7.3% vs 4.2%) compared to Caucasians (H4b supported).
Table 4. Representation of ethnic minorities versus Caucasians on television in terms of respect.
Caucascian | Ethnic minority | ||
|---|---|---|---|
Theme | χ2 (10) = 35.849, p < 0.001 | ||
- Family | 10.4% | 12.7% | |
- Justice, police, and safety | 24.8% | 23.8% | |
- Politics | 6.7%a | 3.2% | |
- Health(care) | 8.4%a | 4.0% | |
- Education and Science | 1.6% | 3.0%a | |
- Culture, art, media, and entertainment | 10.2% | 9.9% | |
- Economy, finance, and business | 13.5% | 14.7% | |
- Friendship, romance, and leisure time | 19.5% | 20.8% | |
- Sports | 1.7% | 1.4% | |
- Other | 2.4% | 4.8%a | |
- Unknown | 0.7% | 1.4% | |
n | 1868 | 495 | |
Association with criminality | χ2 (5) = 21.699, p < 0.001 | ||
- Yes, as a perpetrator | 5.5% | 8.5%a | |
- Yes, as an accomplice | 1.4% | 1.0% | |
- Yes, as a bystander | 2.1%a | 0.4% | |
- Yes, as a victim | 4.2% | 7.3%a | |
- Yes, as law enforcement | 13.1% | 13.5% | |
- No | 73.7%a | 69.3% | |
n | 1868 | 495 |
aFrequency significantly exceeded expectations by adjusted standardized residuals.
The expected overrepresentation of the elderly in the theme of health was not found (H6a not supported). Concerning levels of physical intimacy, as can be seen in Table 5, the results reveal significant differences between the age groups. While the representations do, as expected, show zero sexual intimacy for the elderly age group, the actual differences between age groups are tied to the various acts of physical intimacy (H6b not supported).
Table 5. Representation of age on television in terms of respect.
<18 | 19–64 | 65+ | ||
|---|---|---|---|---|
Theme | χ2 (20) = 253.228, p < 0.001 | |||
- Family | 40.1%a | 8.5% | 17.0% | |
- Justice, police, and safety | 2.7% | 26.7%a | 18.5% | |
- Politics | 1.4% | 6.4%a | 3.0% | |
- Health(care) | 1.4% | 7.9% | 9.6% | |
- Education and science | 2.7% | 2.0% | 0.0% | |
- Culture, art, media, and entertainment | 8.8% | 9.8% | 17.8% | |
- Economy, finance, and business | 0.7% | 14.7%a | 10.4% | |
- Friendship, romance, and leisure time | 37.4%a | 18.6% | 17.8% | |
- Sports | 1.4% | 1.8% | 0.0% | |
- Other | 0.7% | 2.9% | 4.4% | |
- Unknown | 2.7% | 0.7% | 1.5% | |
n | 147 | 2130 | 135 | |
Physical intimacy | χ2 (10) = 27.055, p < 0.01 | |||
- Yes, arm around shoulder | 4.8% | 3.6% | 4.4% | |
- Yes, hugging, cuddling and kissing cheek | 13.6%a | 6.0% | 9.6% | |
- Yes, kissing, petting and touching over clothing | 2.7% | 2.9% | 1.5% | |
- Yes, sexual acts | 0.7% | 3.0%a | 0.0% | |
- No | 77.6% | 84.5% | 84.4% | |
- Unknown/Unable to code | 0.7% | 0.1% | 0.0% | |
n | 147 | 2130 | 135 |
aFrequency significantly exceeded expectations by adjusted standardized residuals.
As can be seen in Table 6, none of the expectations regarding sexual orientation were found in the data. There are no significant differences between heterosexuals and sexual minorities in terms of thematic associations, levels of physical intimacy, or their portrayals as victims (H8 not supported).
Table 6. Representation of sexual orientation on television in terms of respect.
Heterosexual | Sexual minority | ||
|---|---|---|---|
Theme | χ2 (9) = 15.618, p = 0.075 | ||
- Family | 27.2% | 22.2% | |
- Justice, police, and safety | 19.1 | 5.6% | |
- Politics | 0.8% | 0.0% | |
- Health(care) | 3.5% | 0.0% | |
- Education and Science | 0.8% | 0.0% | |
- Culture, art, media, and entertainment | 2.7% | 0.0% | |
- Economy, finance, and business | 5.0% | 2.8% | |
- Friendship, romance, and leisure time | 39.3% | 66.7% | |
- Sports | 0.6% | 2.8% | |
- Other | 1.2% | 0.0% | |
- Unknown | - | - | |
n | 519 | 36 | |
Physical intimacy | χ2 (4) = 3.554, p = 0.470 | ||
- Yes, arm around shoulder | 7.3% | 8.3% | |
- Yes, hugging, cuddling and kissing cheek | 15.0% | 16.7% | |
- Yes, kissing, petting and touching over clothing | 11.2% | 16.7% | |
- Yes, sexual acts | 12.1% | 2.8% | |
- No | 54.3% | 55.6% | |
- n | 519 | 36 | |
Association with criminality | χ2 (5) = 9.306, p = 0.097 | ||
- Yes, as a perpetrator | 6.7% | 2.8% | |
- Yes, as an accomplice | 0.4% | 0.0% | |
- Yes, as a bystander | 2.7% | 0.0% | |
- Yes, as a victim | 7.9% | 0.0% | |
- Yes, as law enforcement | 10.8% | 2.8% | |
- No | 71.5% | 94.4% | |
n | 519 | 36 |
Platform differences in recognition and respect
After describing the overall results for the representation of minorities, additional analyses were conducted to test whether the results for recognition and respect varied by platform (Hypothesis 9).
Starting with recognition (see Table 7), we first focus on gender. Contrary to the expectation, women are significantly underrepresented on all platforms. Streaming platforms do not outperform other platforms in terms of recognition for women in their representations. Second, for ethnicity, it was found that both for public and commercial platforms, there are no significant differences between the presence of ethnic minorities in society and their proportions in the cast of television. In contrast, streaming platforms do showcase significant differences in the presence of ethnic minorities in society compared with their representations (streaming 28.7% vs. society 14.8%). However, the data revealed an overrepresentation of ethnic minorities, rather than an underrepresentation. This means that all platforms are giving recognition to ethnic minorities, but streaming platforms are outperforming other platforms in this regard.
Table 7. Presence of minorities on television per platform compared with census.
Public1 | Commercial2 | Streaming3 | Census (CBS) | ||
|---|---|---|---|---|---|
Gendera | |||||
- Male | 61.5% | 62.1% | 64.6% | 49.7% | 1. χ2 (1) = 28.238, p < 0.001 |
- Female | 38.5% | 37.9% | 35.4% | 50.3% | 2. χ2 (1) = 73.296, p < 0.001 |
n | 509 | 1202 | 816 | 17,590,672 | 3. χ2 (1) = 72.144, p < 0.001 |
Ethnicityb | |||||
- White/Caucasian | 82.5% | 83.7% | 71.3% | 85.2% | 1. χ2 (1) = 2.815, p = 0.093 |
- Ethnic minority | 17.5% | 16.3% | 28.7% | 14.8% | 2. χ2 (1) = 2.041, p = 0.153 |
n | 498 | 1186 | 792 | 17,590,672 | 3. χ2 (1) = 120.684, p < 0.001 |
Agec | |||||
- Young ( <18) | 5.7% | 3.7% | 9.1% | 21.2% | 1. χ2 (1) = 60.320, p < 0.001 |
- Adult (19–64) | 88.0% | 89.4% | 87.5% | 58.7% | 2. χ2 (1) = 129.218, p < 0.001 |
- Senior (65 + ) | 6.3% | 6.9% | 3.4% | 20.1% | 3. χ2 (1) = 140.003, p < 0.001 |
n | 510 | 1201 | 814 | 17,590,672 | |
Sexual orientationd | |||||
- Heterosexual | 87.7% | 96.6% | 92.1% | 94.4% | 1. χ2 (1) = 6.972, p = 0.008 |
- Sexual minority | 12.3% | 3.4% | 7.9% | 5.6% | 2. χ2 (1) = 2.256, p = 0.133 |
n | 81 | 238 | 239 | 17,590,672 | 3. χ2 (1) = 2.496, p = 0.114 |
aChi-square was calculated based on a comparison of TV cast (per platform) and census data (CBS, 2022) in which gender was reduced to a binary category, excluding categories of other and unknown.
bEthnicity was calculated based on a comparison of TV cast per platform with CBS 2022 data of Caucasian and non-western allochthonous proportions. TV data categories of unclear or unknown were not used in the analysis.
cChi-square was calculated based on a comparison of TV cast (senior/non-senior) with CBS 2022 data of non-senior (<65) and senior (65 >), excluding categories of other and unknown.
dReal-life proportions of sexual minority orientation (5.6% LHBT) were based on SCP data (Huijnk, 2022), 2010; sexual orientation of TV cast is a subsample which reduced to either sexual minority or heterosexual, excluding category unknown.
Third, for age, it was found that the elderly are significantly underrepresented compared to societal proportions on each of the platforms. Streaming platforms do not outperform other platforms in terms of recognition for the elderly in their representations. Finally, sexual minorities are significantly overrepresented by public broadcasters compared to their societal presence (public 12.3% vs. society 5.6%), and on commercial and streaming platforms, their presence is on par with societal proportions. This means that contrary to expectation, public broadcasters, rather than streaming platforms, outperform the other platforms in terms of recognition of sexual minorities in their representations.
In terms of respect, as can be seen in Table 8, a platform-specific analysis revealed that women are significantly overrepresented in the theme of family on all platforms and in the theme of interpersonal relationships only on commercial platforms. The results showed that there are no differences in terms of occupation for men and women on commercial platforms, while on public platforms women are more often shown not working or their occupational roles are unknown compared to men. Streaming platforms also showcase significant differences in the presentation of working roles in that men are overrepresented as working, and for women, this is significantly more often unknown. Concerning their role as romantic partners, only on streaming platforms is this role significantly more prominent for women than men. Concerning parental status and household and caregiving tasks, there are no significant differences between men and women on public platforms. For parental status, women are overrepresented both on commercial and streaming platforms in this role, and for household and caregiving tasks similar results were found.
Table 8. Gender representation per television platform in terms of respect.
Public1 | Commercial2 | Streaming3 | |||||
|---|---|---|---|---|---|---|---|
♂ | ♀ | ♂ | ♀ | ♂ | ♀ | ||
Theme | |||||||
- Family | 3.2% | 8.0%a | 5.2% | 11.1%a | 14.1% | 27.3%a | 1. χ2 (10) = 23.369, p < 0.01 |
- Justice, police, and safety | 16.4% | 13.8% | 31.5%a | 20.1% | 32.5%a | 17.3% | 2. χ2 (9) = 71.120, p < 0.001 |
- Politics | 18.2% | 15.5% | 2.7% | 1.6% | 5.4% | 3.5% | 3. χ2 (10) = 46.995, p < 0.001 |
- Health(care) | 8.6% | 6.9% | 7.0% | 16.5%a | 2.5% | 3.8% | |
- Education and science | 2.5% | 2.3% | 0.7% | 0.2% | 3.3% | 4.5% | |
- Culture, art, media, and entertainment | 16.8% | 24.1% | 10.8% | 13.3% | 2.9% | 2.8% | |
- Economy, finance, and business | 12.5%a | 3.4% | 19.9%a | 10.8% | 12.0% | 12.8% | |
- Friendship, romance, and leisure time | 16.1% | 22.4% | 17.2% | 22.3%a | 19.5% | 23.9% | |
- Sports | 2.9% | 1.1% | 1.6% | 0.9% | 2.3% | 1.0% | |
- Other | 2.5% | 2.3% | 3.5% | 3.2% | 3.3%a | 0.3% | |
- Unknown | 0.4% | 0.0% | - | - | 2.3% | 2.8% | |
n | 280 | 174 | 705 | 443 | 523 | 289 | |
Relationship | |||||||
- Yes | 8.6% | 11.5% | 9.4% | 12.6% | 14.0% | 25.6%a | 1. χ2 (2) = 5.923, p = 0.052 |
- No | 4.3% | 9.2%a | 7.0% | 7.2% | 9.4% | 9.0% | 2. χ2 (2) = 3.199, p = 0.202 |
- Unknown | 87.1% | 79.3%a | 83.7% | 80.1% | 76.7%a | 65.4% | 3. χ2 (2) = 17.234, p < 0.001 |
n | 280 | 174 | 705 | 443 | 523 | 289 | |
Parental status | |||||||
- Yes | 7.9% | 13.2% | 6.7% | 11.3%a | 12.8% | 21.8%a | 1. χ2 (1) = 3.454, p = 0.063 |
- No | 92.1% | 86.8% | 93.3%a | 88.7% | 87.2%a | 78.2% | 2. χ2 (1) = 7.507, p < 0.01 |
280 | 174 | 705 | 443 | 523 | 289 | 3. χ2 (1) = 11.184, p < 0.001 | |
n | |||||||
Occupation | |||||||
- Yes | 65.7%a | 41.1% | 59.4% | 56.2% | 58.7%a | 43.6% | 1. χ2 (2) = 27.787, p < 0.001 |
- No | 0.7% | 3.4%a | 4.3% | 4.7% | 6.3% | 9.7% | 2. χ2 (2) = 1.177, p = 0.555 |
- Unknown | 33.6% | 55.2%a | 36.3% | 39.1% | 35.0% | 46.7%a | 3. χ2 (2) = 17.321, p < 0.001 |
n | 280 | 174 | 705 | 443 | 523 | 289 | |
Household & caregiving tasks | |||||||
- Yes | 3.2% | 4.0% | 4.0% | 8.6%a | 6.1% | 16.3%a | 1. χ2 (1) = 0.206, p = 0.650 |
- No | 96.8% | 96.0% | 96.0%a | 91.4% | 93.9%a | 83.7% | 2. χ2 (1) = 10.653, p < 0.001 |
n | 280 | 174 | 705 | 443 | 523 | 289 | 3. χ2 (1) = 21.811, p < 0.001 |
aFrequency significantly exceeded expectations by adjusted standardized residuals.
Concerning the stereotyped association of ethnic minorities with criminality, the platform-specific analyses revealed that these expected differences were not found on any of the platforms. Furthermore, the association of ethnic minorities with being a victim or perpetrator of criminality was only found to be significant for being a perpetrator on commercial platforms. These associations were not found on streaming platforms or public broadcasters (Table 9).
Table 9. Representation of ethnic minorities (MIN) versus Caucasians (CA) per television platform in terms of respect.
Public1 | Commercial2 | Streaming3 | |||||
|---|---|---|---|---|---|---|---|
CA | MIN | CA | MIN | CA | MIN | ||
Theme | |||||||
- Family | 4.4% | 6.3% | 7.9% | 5.9% | 18.5% | 20.7% | 1. χ2 (10) = 25.054, p < 0.01 |
- Justice, police, and safety | 17.4% | 8.8% | 25.6% | 31.4% | 28.3% | 22.9% | 2. χ2 (9) = 16.787, p = 0.052 |
- Politics | 20.1%a | 6.3% | 2.5% | 1.1% | 5.2% | 4.0% | 3. χ2 (10) = 15.543, p = 0.113 |
- Health(care) | 8.3% | 5.0% | 11.7% | 5.3% | 2.9% | 2.6% | |
- Education and Science | 1.9% | 3.8% | 0.6% | 0.0% | 3.0% | 5.3% | |
- Culture, art, media, and entertainment | 16.5% | 28.7%a | 11.9% | 12.2% | 3.4% | 1.3% | |
- Economy, finance, and business | 9.4% | 8.8% | 16.3% | 18.1% | 11.6% | 14.1% | |
- Friendship, romance, and leisure time | 17.9% | 23.8% | 19.3% | 18.1% | 20.9% | 22.0% | |
- Sports | 2.2% | 2.5% | 1.2% | 2.1% | 2.3% | 0.4% | |
- Other | 1.9% | 5.0% | 3.0% | 5.9% | 1.6% | 4.0% | |
- Unknown | 0.0% | 1.3%a | - | - | 2.3% | 2.6% | |
n | 363 | 80 | 944 | 188 | 561 | 227 | |
Association with criminality | |||||||
- Yes, as a perpetrator | 1.9% | 2.5% | 5.3% | 10.6%a | 8.0% | 8.8% | 1. χ2 (5) = 5.329, p = 0.377 |
- Yes, as an accomplice | 1.7% | 0.0% | 1.7% | 1.6% | 0.9% | 0.9% | 2. χ2 (5) = 16.034, p < 0.01 |
- Yes, as a bystander | 2.8% | 1.3% | 1.8% | 0.0% | 2.1% | 0.4% | 3. χ2 (5) = 4.064, p = 0.540 |
- Yes, as a victim | 1.7% | 5.0% | 3.9% | 6.9% | 6.2% | 8.4% | |
- Yes, as law enforcement | 6.9% | 7.5% | 16.8% | 19.1% | 10.9% | 11.0% | |
- No | 85.1% | 83.8% | 70.4%a | 61.7% | 71.8% | 70.5% | |
n | 363 | 80 | 944 | 188 | 561 | 227 | |
aFrequency significantly exceeded expectations by adjusted standardized residuals.
Concerning the stereotyped association of the elderly with health themes, the platform-specific analyses revealed that these expected differences were only found to be significant on streaming platforms. Furthermore, the expected lack of sexual intimacy for the elderly was found on all platforms. There were no significant differences between age groups in levels of intimacy portrayed on public broadcasters, while the levels of intimacy varied significantly for age groups on commercial and streaming platforms, but these were not specifically tied to the elderly (see Table 10).
Table 10. Representation of age per television platform in terms of respect.
Public1 | Commercial2 | Streaming3 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
<18 | 19–64 | 65+ | <18 | 19–64 | 65+ | <18 | 19–64 | 65+ | ||
Theme | ||||||||||
- Family | 20.7%a | 3.8% | 7.7% | 38.6%a | 6.0% | 9.9% | 48.6%a | 14.7% | 46.4% | 1. χ2 (20) = 48.857, p < 0.001 |
- Justice, police, and safety | 0.0% | 17.0%a | 7.7% | 4.5% | 28.3%a | 23.5% | 2.7% | 29.9%a | 14.3% | 2. χ2 (18) = 118.817, p < 0.001 |
- Politics | 6.9% | 18.3% | 11.5% | 0.0% | 2.5% | 0.0% | 0.0% | 5.2% | 3.6% | 3. χ2 (20) = 115.026, p < 0.001 |
- Health(care) | 6.9% | 8.5% | 3.8% | 0.0% | 11.1% | 11.1% | 0.0% | 3.0% | 10.7%a | |
- Education and science | 0.0% | 2.8% | 0.0% | 0.0% | 0.6% | 0.0% | 5.4% | 3.7% | 0.0% | |
- Culture, art, media, and entertainment | 34.5% | 16.5% | 46.2%a | 4.5% | 11.8% | 14.8% | 1.4% | 3.1% | 0.0% | |
- Economy, finance, and business | 0.0% | 10.3%a | 0.0% | 0.0% | 17.0% | 17.3% | 1.4% | 14.0%a | 0.0% | |
- Friendship, romance, and leisure time | 24.1% | 18.3% | 19.2% | 52.3%a | 17.8% | 18.5% | 33.8%a | 20.1% | 14.3% | |
- Sports | 3.4% | 2.3% | 0.0% | 0.0% | 1.5% | 0.0% | 1.4% | 2.0% | 0.0% | |
- Other | 3.4% | 2.3% | 3.8% | 0.0% | 3.4% | 4.9% | 0.0% | 2.4% | 3.6% | |
- Unknown | 0.0% | 0.3% | 0.0% | - | - | - | 5.4% | 2.0% | 7.1% | |
n | 29 | 400 | 26 | 44 | 1022 | 81 | 74 | 708 | 28 | |
Physical intimacy | ||||||||||
- Yes, arm around shoulder | 0.0% | 1.0% | 3.8% | 0.0% | 4.6% | 2.5% | 9.5%a | 3.5% | 10.7% | 1. χ2 (10) = 6.539, p = 0.768 |
- Yes, hugging, cuddling and kissing cheek | 3.4% | 1.0% | 0.0% | 25.0%a | 6.4% | 9.9% | 10.8% | 8.2% | 17.9% | 2. χ2 (8) = 30.669, p < 0.001 |
- Yes, kissing, petting and touching over clothing | 0.0% | 2.5% | 0.0% | 6.8% | 2.4% | 2.5% | 1.4% | 3.7% | 0.0% | 3. χ2 (10) = 22.985, p < 0.05 |
- Yes, sexual acts | 3.4% | 1.5% | 0.0% | 0.0% | 2.2% | 0.0% | 0.0% | 5.1%a | 0.0% | |
- No | 93.1% | 93.8% | 96.2% | 68.2% | 84.4% | 85.2% | 77.0% | 79.4% | 71.4% | |
- Unknown/Unable to code | 0.0% | 0.3% | 0.0% | 0.0% | 0.0% | 0.0% | 1.4% | 0.1% | 0.0% | |
n | 29 | 400 | 26 | 44 | 1022 | 81 | 74 | 708 | 28 | |
aFrequency significantly exceeded expectations by adjusted standardized residuals.
Sexual minorities are overrepresented when it comes to themes related to interpersonal relationships solely on popular streaming platforms. Overall, there are no significant differences between heterosexuals and sexual minorities when comparing the various forms of physical intimacy, even though; comparatively, sexual minorities rarely engage in explicit sex. Platform-specific analysis revealed that on commercial broadcasters, sexual minorities are overrepresented in hugging as a form of physical intimacy compared to heterosexuals. No other significant platform-specific differences were found for physical intimacy. It is worth noting that on public and commercial broadcasters, there were zero sexual acts recorded. Finally, the expected overrepresentation of sexual minorities as victims of crime was not found on any of the different platforms Table 11.
Table 11. Representation of sexual minorities (MIN) versus heterosexuals (HET) per television platform in terms of respect.
Public1 | Commercial2 | Streaming3 | |||||
|---|---|---|---|---|---|---|---|
HET | MIN | HET | MIN | HET | MIN | ||
Theme | |||||||
- Family | 9.9% | 44.4%a | 19.3% | 12.5% | 40.9%a | 15.8% | 1. χ2 (7) = 10.573, p = 0.158 |
- Justice, police, and safety | 9.9% | 0.0% | 19.7% | 0.0% | 21.4% | 10.55 | 2. χ2 (7) = 9.935, p = 0.192 |
- Politics | 2.8% | 0.0% | - | - | 0.9% | 0.0% | 3. χ2 (8) = 18.488, p < 0.05 |
- Health(care) | 1.4% | 0.0% | 5.3% | 0.0% | 2.3% | 0.0% | |
- Education and Science | - | - | 0.9% | 0.0% | 0.9% | 0.0% | |
- Culture, art, media, and entertainment | 12.7% | 0.0% | 1.8% | 0.0% | 0.5% | 0.0% | |
- Economy, finance, and business | 4.2% | 11.1% | 5.7% | 0.0% | 4.5% | 0.0% | |
- Friendship, romance, and leisure time | 56.3% | 44.4% | 46.1% | 75.0% | 26.8% | 73.7%a | |
- Sports | - | - | 1.3% | 12.5% | - | - | |
- Other | 2.8% | 0.0% | - | - | 1.8% | 0.0% | |
- Unknown | - | - | - | - | - | - | |
n | 71 | 9 | 228 | 8 | 220 | 19 | |
Physical intimacy | |||||||
- Yes, arm around shoulder | 2.8% | 0.0% | 7.0% | 0.0% | 9.1% | 15.8% | 1. χ2 (4) = 2.475, p = 0.649 |
- Yes, hugging, cuddling and kissing cheek | 5.6% | 0.0% | 14.5% | 50.0%a | 18.6% | 10.5% | 2. χ2 (4) = 9.927, p < 0.05 |
- Yes, kissing, petting and touching over clothing | 11.3% | 22.2% | 12.3% | 25.0% | 10.0% | 10.5% | 3. χ2 (4) = 3.150, p = 0.533 |
- Yes, sexual acts | 9.9% | 0.0% | 9.6% | 0.0% | 15.5% | 5.3% | |
- No | 70.4% | 77.8% | 56.6% | 25.0% | 46.8% | 57.9% | |
n | 71 | 9 | 228 | 8 | 220 | 19 | |
Association with criminality | |||||||
- Yes, as a perpetrator | 2.8% | 0.0% | 7.5% | 0.0% | 7.3% | 5.3% | 1. χ2 (5) = 2.736, p = 0.603 |
- Yes, as an accomplice | 0.0% | 0.0% | 0.4% | 0.0% | 0.5% | 0.0% | 2. χ2 (5) = 3.563, p = 0.614 |
- Yes, as a bystander | 8.5% | 0.0% | 1.8% | 0.0% | 1.8% | 0.0% | 3. χ2 (5) = 3.279, p = 0.657 |
- Yes, as a victim | 5.6% | 0.0% | 6.1% | 0.0% | 10.5% | 0.0% | |
- Yes, as law enforcement | 7.0% | 0.0% | 15.4% | 0.0% | 7.3% | 5.3% | |
- No | 76.1% | 100% | 68.9% | 100% | 72.7% | 89.5% | |
n | 71 | 9 | 228 | 8 | 220 | 19 | |
aFrequency significantly exceeded expectations by adjusted standardized residuals.
Taken together, we can conclude that streaming platforms do not outperform other platforms in terms of recognition and respect for women in their representations (H9a not supported). For ethnic minorities, streaming platforms do outperform other platforms in terms of recognition for ethnic minorities in their representations. The negative outlier in terms of respect in the representation of ethnic minorities are commercial broadcasters, while public and streaming platforms show very similar results for both Caucasians and ethnic minorities in terms of respect (H9b partially supported). For age, we can conclude that streaming platforms do not outperform other platforms in terms of recognition and respect for the elderly in their representations (H9c not supported). Finally, sexual minorities are awarded recognition on all platforms, and in terms of respect the only significant indicator that was lacking herein was a stereotyped association on streaming platforms with themes related to interpersonal relationships. None of the other indicators on any of the platforms showed significant differences with heterosexuals (H9d not supported).
Discussion
With this study, we aimed to analyze the representation of minority groups, through the lens of recognition and respect (Signorielli and Bacue, 1999; Lauzen and Dozier, 2005), on Dutch prime-time television as well as the most popular streaming platforms in the Netherlands. Our study reveals that on Dutch prime-time television and popular streaming platforms, women and seniors are underrepresented when compared to societal proportions. Their representation remains lacking in recognition. The representation of sexual minorities and ethnic minorities is on par with societal proportions on all platforms and is therefore given recognition in this regard. These findings are in line with previous research, where representations of particularly women are shown to be resistant to change, while for ethnic and sexual minorities, slight improvements in recognition have been noted before (Emons et al., 2010; Daalmans and ter Horst, 2017; See Jane, 2021; Kessler et al., 2004; Segijn et al., 2014; Signorielli, 2001, 2004; Signorielli and Bacue, 2009).
Although small differences in the representation of minorities in terms of respect have been found across platforms, the portrayal of women remains the most stereotypical across platforms. All in all, our results reveal that neither public nor commercial broadcasters nor popular streaming platforms succeed in being a fully inclusive, diverse, equitable, and representative reflection of Dutch society. This means that regardless of the platform we watch television on, some minority groups remain underrepresented and stereotypically portrayed, and the promises made towards diversity and inclusion on streaming platforms and legacy broadcasters have still not been fulfilled (Bucciferro, 2019; Pietaryte and Suzina, 2023; Sarandos, 2021; Smith et al., 2021).
With the results of this study, we have more clearly outlined what the representation of minority groups looks like in terms of recognition and respect for the Dutch context. In consideration of the changing television landscape, we examined whether recent commitments to diversity and inclusion have sown rewards in popular programming, looking at both traditional broadcasters and popular streaming platforms. By looking at those platforms separately, with regard also to the increased control people have over what they choose to watch in terms of channels, genres, and programs, future research can more explicitly study which effects (following cultivation as well as social learning theory) these (stereotyped) portrayals have on their viewers.
While we have looked at programming on a very abstract level, future research might also tease out differences on a genre level. Our sample hosts a variety of genres, which also shows variance per specific platform. Previous research has established variance in how genres represent different minority groups and that within broadcasters, there might be differences in the representation of minorities per subchannel (Daalmans and ter Horst, 2017; Daalmans, Kleemans, and Sadza, 2017; Segijn et al. 2014; Signorielli, 2009). Therefore, to create a more fully comprehensive picture, these analyses might be used in future research.
Future research might also take on more various foci (e.g., global, transnational, regional and local) in analyzing country differences within the representation of minorities in the broadcasting landscape since much of the programming, both on public channels and commercial channels, is originally not made in or for the Netherlands, but in countries such as the U.S., U.K., Germany, Belgium, and the Scandinavian countries. As such, one might expect national differences between and within the type of broadcaster, if it contains a large degree of foreign-made programs, since “every country’s television system reflects the historical, political, social, economic, and cultural contexts within which it has developed” (Gerbner et al., 1978, p. 178). Kuipers (2008) reported in her study on American fiction on Dutch television that American fiction comprised about a third of the fiction programs on public channels and 79% of the fiction on commercial channels. With the addition of streaming platforms, this distinction might become even more interesting since for example Netflix showcases a large variety of nations in their content creation, and reporting that aside from US-made content, South Korean content as well as Spanish-spoken content has become more popular over time (Moore, 2022). This was also visible in the build-up of the current sample, where American programs dominated much of the sample, but this differed significantly per broadcaster. The public broadcasters had 93% Dutch-origin programming (3.7% US programming), commercial broadcasters 42.1% Dutch-origin programming (46.0% US programming) and streaming 16.9% Dutch-origin programming (75.7% US programming). Therefore, a closer analysis of how country of origing impacts the representation of minorities, within a specific country or from a cross-cultural perspective, might be a fruitful avenue for further research.
Finally, starting January 1, 2024, major streaming services active in the Netherlands must invest 5% of their annual turnover in Dutch audiovisual productions such as series, films, and documentaries. How this will affect minority representation and the general breadth of local versus regional versus global programming within streaming programming deserves continued scholarly attention (Rijksoverheid, 2023).
As with all studies, some limitations must be noted. Firstly, the analysis was carried out with dichotomous categories, meaning that the variables were reduced to only two or three levels at the expense of the richness of data. Future studies could therefore further explore the recognition and respect of more fine-grained subgroups within minority groups.
Second, in terms of variables we coded a host of aspects that—based on previous research—were related to the central concepts of recognition and respect. To this end, we closely replicated previous codebooks used in the analysis of minority representation in the operationalization of our variables (Bond et al., 2019; Commissariaat voor de Media, 2021; Daalmans, Kleemans, et al., 2017; Daalmans and ter Horst, 2017; Dillman Carpentier et al., 2017; Elasmar et al., 1999; Fisher et al. 2004; Koeman et al. 2007; Lauzen and Dozier, 2005; Manganello et al., 2008; Oliver, 1994; Pennekamp, 2011; Segijn et al., 2014; Signorielli and Bacue, 1999; Signorielli and Bievenour, 2015). As a result, we were not able to explore all these aspects in an in-depth manner. For example, household and caregiving tasks were coded as either being performed or not performed, and having an occupation was coded as having or not having an occupation or this being unknown. For both variables, you might argue that either the amount of household and caregiving tasks per program could vary tremendously between persons, and the type of work being done by various minority groups could also convey stereotyped messages of worth. Building on previous research to comparatively create a more complete picture of the status of minority representation in the Dutch context was what we set out to do and using measures from previous research enabled us to do so, although we do recommend that future research also include these more subtle and fine-grained measures to create a complete picture of minority representation.
Thirdly, sample selection and data collection for this study were conducted in 2022, and it thereby provides a snapshot of how minorities were represented in prime-time television and popular programs on the most popular streaming platforms in the Netherlands at that specific time. While some portrayals are resistant to change (i.e., women, the elderly), some changes in representation are noted compared to earlier studies. Furthermore, in terms of the timespan captured by the sample, the study is (similar to other studies using a similar sampling strategy, Daalmans and ter Horst, 2017; Daalmans, Kleemans and Sadza, 2017; Emons et al. 2010; Koeman et al., 2007) relatively narrow in timespan, and does not account for a longer period of time in one year or over years, and as such does not take into account changing catalogs, special broadcasting periods or seasonal differences in programming. It may, therefore, be valuable to conduct content analyses of both prime-time programming and on-demand platforms at more regular intervals.
Finally, regarding the inclusion strategy of programs from the most popular streaming platforms, one might be critical about the construction of new accounts to assess the input for the sample. We chose to include the most popular and top-10 lists that were shown to viewers when creating a new account; this is not to say that viewers in the Netherlands actually watched these programs. However, similar to the point made by Sadza et al. (2023), the algorithmic nature of these platforms, coupled with their lack of transparency in viewing figures, makes it difficult to define what is popular or most watched. We, therefore, made the pragmatic choice and opted for a naturalistic approach by creating accounts for new users and coding 10 of the most popular programs offered to these user profiles by the algorithm as a proxy for being consumed by many viewers of the streaming platforms. This way, we matched the programs in the sample from streaming platforms to the programs in prime-time programming as best we could since prime-time is historically and empirically seen as the timeslot where most viewers consume television (Krantz-Kent, 2018; Schaper et al., 2019; Smith et al., 2002). While recommender systems play an important role in providing content guidance (Frey, 2021), the lack of viewing figures means we cannot be sure that these popular programs are also the programs that were most watched. Future research might investigate how we can reliably determine viewing figures of streaming content. Following Sadza et al. (2023), we would recommend that future research start by creating multiple accounts per streaming platform using different demographic settings (where possible) to create an even more naturalistic reflection of the content offered to users per platform and build a sample from there.
As television continues to dominate our media use, both through traditional broadcasting platforms as well as the increasingly popular streaming platforms, and even though viewers now have more control over what they watch than ever before, it still easily follows that television viewing has a demonstrable impact on how the public at large views minorities as well as on the way minorities see themselves. And since this study, following a plethora of studies, reveals that even though some changes have been made towards more reflective representation, we still have work to do before these representations are accurate, inclusive, and equitable representations of various minority groups, it is important that we continue to analyze how these (mis)representations affect people in real life.
Author contributions
SD and MK contributed to the conception and design of the study. SD and RH collected the data. SD and RH conducted the analyses. SD wrote the first draft of the manuscript. RH and MK provided feedback on the manuscript and rewrote sections. All authors contributed to manuscript revision, read, and approved the submitted version. The authors want to thank Cedra van Erp and Anne Vlaanderen for their help in the coding of the programs.
Data availability
The pre-registration of the study, codebook, coding procedures, sample overview, and dataset are available via OSF: https://osf.io/xed3f/.
Competing interests
The authors declare no competing interests.
Ethical approval
Ethical approval was not required as the study did not involve human participants.
Informed consent
Informed consent was not required as the study did not involve human participants.
https://aspredicted.org/748my.pdf
2https://osf.io/xed3f/
3Entire codebook and coding procedures can be found on OSF, via: https://osf.io/xed3f/
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Abstract
This study focused on the representation of minority groups on television, following the idea(l) that television as a mirror of society should convey a well-balanced representation of society. The current study extends previous research in that it analyses the potential for differences between regular broadcasters and streaming platforms, due to the latter’s public commitments to diversity and inclusion. Our results reveal that on Dutch prime-time television and popular streaming platforms women and seniors are underrepresented, whilst sexual minorities are overrepresented on public broadcasters and ethnic minorities on all platforms. Furthermore, results revealed that public nor commercial broadcasters nor popular streaming platforms succeed in being a fully inclusive, diverse, equitable, and representative reflection of Dutch society.
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Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
; Haverkort, Rhanna 1 ; Kleemans, Mariska 1 1 Radboud University Nijmegen, Behavioural Science Institute, Nijmegen, the Netherlands (GRID:grid.5590.9) (ISNI:0000 0001 2293 1605)