Bibliographic record
Abstract
The importance of gender in popular Indian cinema has been noted by a number of scholars, and indeed, even a casual observer may find it difficult to overlook. There are, of course, some significant differences between the Indian cinema of yesteryear and today’s popular Indian films, which are often aimed at a diasporic audience. In an effort to capture this audience, Bollywood films represent heroines who are not always as demure and submissive as in older films; many are assertive, educated and successful. A few engage in behaviour that would previously have been seen as taboo, including premarital sex. Yet beneath any veneer of equality, Bollywood films continue to portray gender, particularly femininity, in ways that are regressive. Despite their highly problematic depiction of gender, Bollywood films seem to have a significant effect on the way that some young adults living in diaspora understand femininity, masculinity and relationships, as indicated by findings from a recent study that I conducted on the reception of Bollywood films by young Canadians of South Asian origin. Although gender was not the focus of the study, some of the participants suggested that Bollywood was most significant to them in terms of the views it advanced about women. I argue here that while Bollywood cinema has made limited strides in its portrayal of women, its depictions may have had the unintended and positive effect of igniting feminist debate, however problematic, among some of its diasporic viewers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".