Discursive Construction of New Female Identity in Latest Hollywood Blockbuster Movies
Bibliographic record
Abstract
This study analyses the emergence of female portrayal in latest Hollywood superhero movies, after the #MeToo global movement about awareness of sexual harassment. This research adopts a qualitative approach in analysing the constructs of doing and undoing gender in blockbuster movies by Marvel and DC comics. This study seeks to explore the shift towards discursive and screen empowerment of female lead and supporting characters. Such movies serve as a barometer of the cultural and social milieu and hence project how women can display range of capabilities, independence and emotional strength on screen, so to pave the way for viewers. The premise of this paper is rooted in events following 9/11 and how blockbuster films helped in social uplifting by showing solutions till date. All such attempts of social restorations were led by all-male teams of superheroes, the events in recent couple of years are looking quite different. The discussion is rooted in transition from Zimmerman’s idea of ‘doing gender’ (1997), for social conformity, to Deutsch’s proposal of ‘undoing gender’ (2007), where females adopt a powerful position and voice. This approach resonates with latest, or fourth wave, of feminism. The emergence of able-minded stronger women is taking over and shaping new Man, a flexible, emotional and imperfect male. The paper also glimpses into other genres and studios’ movies of the recent times to find signs of change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".