Gender portrayal in popular music videos: a comparative analysis of India and Canada
Bibliographic record
Abstract
Popular culture is a powerful form of public pedagogy (Giroux, 2004a; 2004b). That is, people learn from pop culture even when they think they are just being “entertained.” Considering the prevalence of media in the current context, popular culture may serve as a more influential pedagogue than official curricula or teachers (Moore, 2014). Grounded in feminist and queer media literacies, this study used critical content analysis and QSs to analyse popular music videos from Canada and India. The analysis considered: a) the ways that gender is constructed through popular music videos in two distinct contexts; b) the potential messages about gender communicated in music videos; and c) the way popular music videos could be used in formal classroom spaces to ground broader conversations about gender. The critical content analysis and qualitative survey revealed the presence of gender stereotypes and heteronormative representations within most character relationships with power lying in the hands of male characters. This research created a Basic Inclusivity Audit Survey (B.I.A.S) to observe and practice gender-based analysis of media texts for further discussions on the portrayal of gender in Media.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".