Women, labor and television: a critical analysis of women portrayed in Pakistani drama serials
Bibliographic record
Abstract
The purpose of this research is to look critically at the way women are represented in mainstream entertainment media on Pakistani television channels, mainly in televised drama serials. My chief research question is: How is women’s labor represented in select Pakistani televised drama serials? One reason women’s work is not always apparent is because of the gendered nature of work, and especially care work, where it is assumed that women will look after the household chores, especially in patriarchal societies. I propose that these shows (1) naturalize a certain form or notion of femininity as the only suitable one for Pakistan’s women, and (2) naturalize a theory of work that both denies the actual work that women do and that discourages women from stepping into the masculinist marketplace of public careers. I use feminist media representation theory. Because media has the power to disseminate patriarchal and ideological views, it has always been at the centre of feminist criticism. Feminist audiovisual content analysis enables me to critically highlight the biases towards women employed in different occupations, as well as if and how the social reproductive labor of the women who are portrayed as mothers, sisters, daughters, wives, and grandmothers on screen is ignored or taken for granted.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".