Gender Exclusion in Textbooks: A Comparative Study of Female Representation in Provincial ELT Textbooks of Pakistan
Bibliographic record
Abstract
The focus of this study was female gender representation in secondary level ELT textbooks published by four different textbook boards of Pakistan, namely Baluchistan Textbook Board, Sindh Textbook Board, Khyber Pakhtunkhwah Textbook Board and Punjab Textbook Board. It targeted a comprehensive comparison between the female gender images as represented in four sets of textbooks and gender conceptions of their respective female readers. To achieve the objectives, the study was divided into two parts: In part 1, the textbooks by four state-run textbook boards were analyzed and in part 2, their respective female readers’ gender conceptions were collected and analyzed. The study employed multi-dimensional analytical tools like manifest, latent analysis and Fairclough (2001) CDA model for interpretation and explanation of textbook discourse. The study revealed a low representation share of female gender in four sets of textbooks. It brought out that female readership had stereotype conceptions regarding the attributes, professions and activities as appropriate for the female gender. It was also found that Sindh and Punjab Textbook Boards had improved female gender representation than other provincial textbook boards. The quantitative findings of part 2 proposed that textbooks could play a vital part in modeling gender conceptions of readership as Sindh and Punjab Textbook Boards’ female readership showed better gender conceptions. The study recommended a gender-based test of the textbooks at national level prior to publication to ensure gender equality as directed in National Curriculum.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".