Bibliographic record
Abstract
Intersectionality, the relations among different social variables and their interplay, is an inevitable and most employed way of analyzing the gender related issues. We also consider the intersectionality as a great window to new horizons for gender equality aspirations and research. By modifying the more traditional type of intersectional research methodology, we could build up a solid framework for future studies on gender issues particular to the Middle-East. Our goal is to offer a modern model for intersectional studies specific to middle-east, brining onboard different perspectives, usually neglected in mainstream intersectional studies on gender. This model provides a firm ground for psychological questions touching individual as well interpersonal and social dimensions. For this, we concentrate on Iran where the feminist movement is growing fast and there are reasons to believe that it has religious and social texture similar to the rest of the region. We used intersectional methodology as we consider this the most suitable for gender studies. This paper presents a novel model which offers a great unique opportunity to understand the complexity of factors involving the gender issues in a region which is growing fast but still clings to traditions. We further illustrate how the language plays a role in implementing governmental policy which brings about changes in identity and gender inequality.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.008 |
| 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".