Genderwashing in Pakistani Higher Educational Institutions
Bibliographic record
Abstract
Abstract In this chapter, we critically interrogate Pakistani Higher Education Commission (HEC)'s “Policy Guidelines against Sexual Harassment in Institutions of Higher Learning” (HEC, 2011, 2020) to reveal that the policy represents a form of rhetorical genderwashing, rather than a concerted attempt to effect structural change (Fox-Kirk et al., 2020). Through this critical interrogation, we introduce a new methodological approach to investigate genderwashing, that of collaborative feminist policy analysis. This chapter is based on the theoretical framework of Sara Ahmed's concept of “institutional nonperformativity” (2012). Pakistan recognized workplace harassment as a legal issue for the first time in the 2010 Sexual Harassment Act (Jabbar & Imran, 2013). The Act, and subsequent policies, was an attempt to practice equity and fairness in sexual harassment cases. Our investigation revealed that the policy reinforces gender power inequality, gendered language, and is an example of institutional silence, and genderwashing in the workplace (Fox-Kirk et al., 2020).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".