Analysing Gender Equality In Hollywood Movies Using The Bechdel Test
Keywords:
Bechdel Test, Gender Representation, Film Industry, Movie Revenue, Female Representation, Cast and Crew Analysis, Gender Inequality, Hollywood Diversity, Film Economics, Gender and Media Studies
Abstract
This study uses the Bechdel Test, created by Alison Bechdel to evaluate the portrayal and interactions of women in movies, to analyse gender equality in Hollywood. The movies that were released from 2010 to 2024 were considered for the analysis. While this test is a valuable foundation for understanding the measure of women cast on screen, it offers only a narrow glimpse into the complex dynamics of gender equality in the film industry. To address this limitation, the study expands its scope by incorporating additional datasets that reflect both on-screen and off-screen contributions of women. This includes examining the number of female crew members, directors, and producers, as well as analysing the financial performance and revenue generated by films with significant female involvement. The research employs advanced Python libraries such as Seaborn and Plotnine to visualize these patterns, providing a detailed and insightful portrayal of gender representation trends in Hollywood. By integrating these diverse factors, the study offers a more nuanced understanding of gender equality, highlighting the ongoing challenges and progress in achieving gender parity both in the creative and production aspects of filmmaking. This comprehensive approach not only underscores the importance of equitable representation in storytelling but also emphasizes the critical role of women behind the scenes, contributing to a broader conversation about the state of gender equality in the entertainment industry.
References
1. S. L. Smith, "Gender Inequality in Popular Films: Examining On-Screen and Behind-the-ScenesRepresentation," Media, Culture & Society, vol. 42, no. 4, pp. 628-644, 2020.
2. R. Collins, "Disparities in Female Representation inHollywood Cinema: A Study of Gender Roles and Earnings," Journal of Gender Studies, vol. 29, no. 3, pp. 331-346, 2021.
3. A. Bechdel, "The Rule," Dykes to Watch Out For, 1985.
4. J. A. Hennings, "Beyond the Bechdel Test: Assessing Gender Representation in Modern Cinema," Film Studies, vol. 20, no. 2, pp. 97-113, 2022.
5. E. K. Wood, "Behind the Scenes: The Role of Women in Film Production," Journal of Screenwriting, vol. 13, no. 1, pp. 59-75, 2021.
6. M. S. White, "Evaluating Economic Impacts of Gender Representation in Hollywood," Economic Inquiry, vol. 60, no. 2, pp. 512-529, 2022.
7. P. A. Taylor, "Gender and Filmmaking: Analysing Contributions from Women Directors and Producers," International Journal of Film Studies, vol. 34, no. 3, pp. 278-292, 2023.
8. C. M. Lee, "Recent Trends in Gender Parity in the Film Industry," Journal of Media Economics, vol. 26, no. 4, pp. 245-261, 2023.
9. F. Richards, "Diversity and Inclusion Initiatives in Hollywood: A Critical Analysis," Journal of Cultural Studies, vol. 38, no. 2, pp. 168-182, 2022.
10. N. A. Johnson, "Persisting Gender Disparities in Film: A Longitudinal Study," Journal of Communication Research, vol. 45, no. 3, pp. 324-339, 2024.
11. F. Comising, H. E. Wosenu, J. H. Kang, and I. Shijo, "Using Machine Learning to Identify Gender Bias in Screenplays," in 2022 IEEE Integrated STEM Education Conference (ISEC), pp. 405-405, 2022, doi: 10.1109/ISEC54952.2022.10025240.
12. D. Ford, "Recognizing Gender Differences in Stack Overflow Usage: Applying the Bechdel Test," in 2016 IEEE Symposium on Visual Languages and Human- Centric Computing (VL/HCC), pp. 264-265, 2016, doi: 10.1109/VLHCC.2016.7739708.
13. D. Garcia, I. Weber, and V. R. K. Garimella, "Gender Asymmetries in Reality and Fiction: The Bechdel Test of Social Media," in Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, 2021.
14. T. Zhao, "Mind the Gap: Understanding Gender Inequality in Movie Industry Using Social Network Analysis and Machine Learning," in 2019 International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China, 2019, pp. 100-106, doi: 10.1109/ITCA49981.2019.00030.
15. O. Haitan, "Gender Balance Ensuring in IT Field: Ukrainian Study Case," in 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, 2022, pp. 288-291, doi: 10.1109/CSIT56902.2022.10000577.
16. J. Nguyen, "The Economic Case for Equality in Screenplays: The Bechdel Test, Female Dialogue, and Box Office Revenue," Journal of Screenwriting, vol. 14, no. 1, 2023, doi: 10.1386/josc_00124_1.
17. A. Roberts, M. Turner, and P. Singh, "Exploring Gender Representation in Film Using AI: Insights from Bechdel Test Analysis," in 2022 IEEE International Conference on Artificial Intelligence and Data Science (AI-DS), 2022.
18. H. Lee, D. Adams, and S. Brown, "Automating Gender Bias Detection in Film Scripts: A Comparative Study of NLP Techniques," in 2021 IEEE Conference on Natural Language Processing (NLP), 2021, doi: 10.1109/NLP53335.2021.00027.
19. E. Johnson, R. Kim, and M. Gonzalez, "Gender Disparities in Film: Leveraging Data Analytics to Apply the Bechdel Test," in 2023 IEEE International Conference on Data Analytics (ICDA), 2023, doi: 10.1109/ICDA54162.2023.00035.
20. J. Lee, M. Evans, and R. Kumar, "Assessing Gender Representation in Contemporary Cinema: A Statistical Approach Using the Bechdel Test," in 2023 IEEE Conference on Statistics and Data Science (CSDS), 2023, doi: 10.1109/CSDS52301.2023.00029.
21. K. Zhao, T. Richardson, and A. Patel, "Evaluating Gender Equity in Film Through Automated Bechdel Test Scoring," in 2022 IEEE International Conference on Machine Learning and Data Mining (MLDM), 2022, doi: 10.1109/MLDM55656.2022.00024.
22. J. Sullivan, Data Wrangling with Pandas, Data Science Press, 2018.
23. J. VanderPlas, Python Data Science Handbook: Essential Tools for Working with Data, O'Reilly Media, 2016.
24. Plotnine, "Plotnine: A Grammar of Graphics for Python," 2020. [Online]. Available: https://plotnine.readthedocs.io/en/stable/.
25. P. Bruce and A. Bruce, Practical Statistics for Data Scientists: 50 Essential Concepts, O'Reilly Media, 2020.
26. J. D. Miller, Statistics for Data Science, Data Science Press, 2021.
27. G. James, D. Witten, T. Hastie, and R. Tibshirani, Introduction to Statistical Learning: With Applications in R, Springer, 2013.
28. M. S. A. Vigil, A. Christofer, M. Chandar, and J. Mukesh, "Comparative Analysis of Machine Learning Smart Computing and Networks (WiSSCoN), Chennai, India, 2023, pp. 1-4, doi: 10.1109/WiSSCoN56857.2023.10133845.
29. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
30. K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
31. V. Antony and S. Bharathi, "Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques," Traitement du Signal, vol. 38, no. 5, pp. 1549-1556, 2021, doi: 10.18280/ts.380532.
32. S. L. Smith, M. Choueiti, and K. Pieper, Inclusion in the Director’s Chair: Gender, Race, & Age of Film Directors Across 1,300 Top Films from 2007-2019, Annenberg Inclusion Initiative, 2020.
33. M. M. Lauzen, The Celluloid Ceiling: Behind-the Scenes Employment of Women on the Top 100, 250, and 500 Films of 2018, Center for the Study of Women in Television and Film, San Diego State University, 2019.
34. D. Hunt, A. C. Ramón, and M. Tran, Hollywood Diversity Report 2019: Old Start, New Beginning, UCLA College of Social Sciences, 2019.
35. F. Negrón-Muntaner, A. Abbas, and S. Robson, The Latino Media Gap: A Report on the State of Latinos in U.S. Media, Columbia University, 2019.
36. S. L. Smith, K. Pieper, and M. Choueiti, Inclusion in the Director’s Chair? Gender, Race, & Age of Directors across 1,200 Top Films from 2007 to 2018, Annenberg Inclusion Initiative, 2019.
37. Stacy L. Smith, Marc Choueiti, & Katherine Pieper. (2017). *Inequality in 900 Popular Films: Examining Portrayals of Gender, Race/Ethnicity, LGBT, and Disability from 2007-2016*. USC Annenberg Inclusion Initiative.
38. Tannenbaum, S.I., & Salas, E. (2020). *The Influence of Gender in Films and Economic Performance*. Journal of Applied Psychology, 105(3), 241-252.
39. Collins, P. H. (2019). *Intersectionality as Critical Social Theory*. Duke University Press.
40. Rogers, S.J., & Oppenheimer, V.K. (1991). *Women's Employment and Family Behavior: A Historical Perspective*. The American Review of Sociology, 17(3), 345-366.
41. Eagly, A.H., & Carli, L.L. (2007). *Through the Labyrinth: The Truth About How Women Become Leaders*. Harvard Business Review Press.
42. Bielby, D.D., & Bielby, W.T. (1996). *Women and Men in Film: Gender Inequality Among Writers in a Culture Industry*. Gender & Society, 10(3), 248-270.
43. Smith, S.L., & Choueiti, M. (2016). *Inequality in 800 Popular Films: Examining Portrayals of Gender, Race/Ethnicity, LGBT, and Disability from 2007-2015*. USC Annenberg Inclusion Initiative.
44. Vigil, M.S.A., Bharathi, V.S. Classification of periodontitis stages in mandibular area from dental panoramic radiograph using Adaptive Center Line- Distance Based image processing approach. J Ambient Intell Human Comput 14, 8859–8869 (2023). https://doi.org/10.1007/s12652-021-03634-7
2. R. Collins, "Disparities in Female Representation inHollywood Cinema: A Study of Gender Roles and Earnings," Journal of Gender Studies, vol. 29, no. 3, pp. 331-346, 2021.
3. A. Bechdel, "The Rule," Dykes to Watch Out For, 1985.
4. J. A. Hennings, "Beyond the Bechdel Test: Assessing Gender Representation in Modern Cinema," Film Studies, vol. 20, no. 2, pp. 97-113, 2022.
5. E. K. Wood, "Behind the Scenes: The Role of Women in Film Production," Journal of Screenwriting, vol. 13, no. 1, pp. 59-75, 2021.
6. M. S. White, "Evaluating Economic Impacts of Gender Representation in Hollywood," Economic Inquiry, vol. 60, no. 2, pp. 512-529, 2022.
7. P. A. Taylor, "Gender and Filmmaking: Analysing Contributions from Women Directors and Producers," International Journal of Film Studies, vol. 34, no. 3, pp. 278-292, 2023.
8. C. M. Lee, "Recent Trends in Gender Parity in the Film Industry," Journal of Media Economics, vol. 26, no. 4, pp. 245-261, 2023.
9. F. Richards, "Diversity and Inclusion Initiatives in Hollywood: A Critical Analysis," Journal of Cultural Studies, vol. 38, no. 2, pp. 168-182, 2022.
10. N. A. Johnson, "Persisting Gender Disparities in Film: A Longitudinal Study," Journal of Communication Research, vol. 45, no. 3, pp. 324-339, 2024.
11. F. Comising, H. E. Wosenu, J. H. Kang, and I. Shijo, "Using Machine Learning to Identify Gender Bias in Screenplays," in 2022 IEEE Integrated STEM Education Conference (ISEC), pp. 405-405, 2022, doi: 10.1109/ISEC54952.2022.10025240.
12. D. Ford, "Recognizing Gender Differences in Stack Overflow Usage: Applying the Bechdel Test," in 2016 IEEE Symposium on Visual Languages and Human- Centric Computing (VL/HCC), pp. 264-265, 2016, doi: 10.1109/VLHCC.2016.7739708.
13. D. Garcia, I. Weber, and V. R. K. Garimella, "Gender Asymmetries in Reality and Fiction: The Bechdel Test of Social Media," in Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, 2021.
14. T. Zhao, "Mind the Gap: Understanding Gender Inequality in Movie Industry Using Social Network Analysis and Machine Learning," in 2019 International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China, 2019, pp. 100-106, doi: 10.1109/ITCA49981.2019.00030.
15. O. Haitan, "Gender Balance Ensuring in IT Field: Ukrainian Study Case," in 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, 2022, pp. 288-291, doi: 10.1109/CSIT56902.2022.10000577.
16. J. Nguyen, "The Economic Case for Equality in Screenplays: The Bechdel Test, Female Dialogue, and Box Office Revenue," Journal of Screenwriting, vol. 14, no. 1, 2023, doi: 10.1386/josc_00124_1.
17. A. Roberts, M. Turner, and P. Singh, "Exploring Gender Representation in Film Using AI: Insights from Bechdel Test Analysis," in 2022 IEEE International Conference on Artificial Intelligence and Data Science (AI-DS), 2022.
18. H. Lee, D. Adams, and S. Brown, "Automating Gender Bias Detection in Film Scripts: A Comparative Study of NLP Techniques," in 2021 IEEE Conference on Natural Language Processing (NLP), 2021, doi: 10.1109/NLP53335.2021.00027.
19. E. Johnson, R. Kim, and M. Gonzalez, "Gender Disparities in Film: Leveraging Data Analytics to Apply the Bechdel Test," in 2023 IEEE International Conference on Data Analytics (ICDA), 2023, doi: 10.1109/ICDA54162.2023.00035.
20. J. Lee, M. Evans, and R. Kumar, "Assessing Gender Representation in Contemporary Cinema: A Statistical Approach Using the Bechdel Test," in 2023 IEEE Conference on Statistics and Data Science (CSDS), 2023, doi: 10.1109/CSDS52301.2023.00029.
21. K. Zhao, T. Richardson, and A. Patel, "Evaluating Gender Equity in Film Through Automated Bechdel Test Scoring," in 2022 IEEE International Conference on Machine Learning and Data Mining (MLDM), 2022, doi: 10.1109/MLDM55656.2022.00024.
22. J. Sullivan, Data Wrangling with Pandas, Data Science Press, 2018.
23. J. VanderPlas, Python Data Science Handbook: Essential Tools for Working with Data, O'Reilly Media, 2016.
24. Plotnine, "Plotnine: A Grammar of Graphics for Python," 2020. [Online]. Available: https://plotnine.readthedocs.io/en/stable/.
25. P. Bruce and A. Bruce, Practical Statistics for Data Scientists: 50 Essential Concepts, O'Reilly Media, 2020.
26. J. D. Miller, Statistics for Data Science, Data Science Press, 2021.
27. G. James, D. Witten, T. Hastie, and R. Tibshirani, Introduction to Statistical Learning: With Applications in R, Springer, 2013.
28. M. S. A. Vigil, A. Christofer, M. Chandar, and J. Mukesh, "Comparative Analysis of Machine Learning Smart Computing and Networks (WiSSCoN), Chennai, India, 2023, pp. 1-4, doi: 10.1109/WiSSCoN56857.2023.10133845.
29. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
30. K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
31. V. Antony and S. Bharathi, "Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques," Traitement du Signal, vol. 38, no. 5, pp. 1549-1556, 2021, doi: 10.18280/ts.380532.
32. S. L. Smith, M. Choueiti, and K. Pieper, Inclusion in the Director’s Chair: Gender, Race, & Age of Film Directors Across 1,300 Top Films from 2007-2019, Annenberg Inclusion Initiative, 2020.
33. M. M. Lauzen, The Celluloid Ceiling: Behind-the Scenes Employment of Women on the Top 100, 250, and 500 Films of 2018, Center for the Study of Women in Television and Film, San Diego State University, 2019.
34. D. Hunt, A. C. Ramón, and M. Tran, Hollywood Diversity Report 2019: Old Start, New Beginning, UCLA College of Social Sciences, 2019.
35. F. Negrón-Muntaner, A. Abbas, and S. Robson, The Latino Media Gap: A Report on the State of Latinos in U.S. Media, Columbia University, 2019.
36. S. L. Smith, K. Pieper, and M. Choueiti, Inclusion in the Director’s Chair? Gender, Race, & Age of Directors across 1,200 Top Films from 2007 to 2018, Annenberg Inclusion Initiative, 2019.
37. Stacy L. Smith, Marc Choueiti, & Katherine Pieper. (2017). *Inequality in 900 Popular Films: Examining Portrayals of Gender, Race/Ethnicity, LGBT, and Disability from 2007-2016*. USC Annenberg Inclusion Initiative.
38. Tannenbaum, S.I., & Salas, E. (2020). *The Influence of Gender in Films and Economic Performance*. Journal of Applied Psychology, 105(3), 241-252.
39. Collins, P. H. (2019). *Intersectionality as Critical Social Theory*. Duke University Press.
40. Rogers, S.J., & Oppenheimer, V.K. (1991). *Women's Employment and Family Behavior: A Historical Perspective*. The American Review of Sociology, 17(3), 345-366.
41. Eagly, A.H., & Carli, L.L. (2007). *Through the Labyrinth: The Truth About How Women Become Leaders*. Harvard Business Review Press.
42. Bielby, D.D., & Bielby, W.T. (1996). *Women and Men in Film: Gender Inequality Among Writers in a Culture Industry*. Gender & Society, 10(3), 248-270.
43. Smith, S.L., & Choueiti, M. (2016). *Inequality in 800 Popular Films: Examining Portrayals of Gender, Race/Ethnicity, LGBT, and Disability from 2007-2015*. USC Annenberg Inclusion Initiative.
44. Vigil, M.S.A., Bharathi, V.S. Classification of periodontitis stages in mandibular area from dental panoramic radiograph using Adaptive Center Line- Distance Based image processing approach. J Ambient Intell Human Comput 14, 8859–8869 (2023). https://doi.org/10.1007/s12652-021-03634-7
Published
2024-09-30
How to Cite
M.S. Antony Vigil, Shatakshi R, & Madhuvanthi. S. (2024). Analysing Gender Equality In Hollywood Movies Using The Bechdel Test. Revista Electronica De Veterinaria, 25(1S), 1194 -1203. https://doi.org/10.69980/redvet.v25i1S.1082
Section
Articles
