Weaving Paths for Policies: An Integrated Analysis of The Gender Equality Framework
Bibliographic record
Abstract
Introduction: The Gender Equality Framework for Higher Education Institutions (HEIs) in Brazil, developed by the British Council, aims to promote gender equality through data collection and the implementation of specific action plans.This framework is significant as it addresses historical disparities in HEIs and fosters inclusive academic environments.Its necessity lies in the urgency to combat wage inequalities, harassment, and inequity in access and retention in STEM.In this context, the article analyzes the framework, its features, and its alignment with the Equality in Leadership for Latin American STEM (ELLAS) project, funded by the IDRC.Methodology: The study follows a qualitative and exploratory approach, based on a document analysis of the framework's guidelines and its connection to the objectives of the ELLAS project.Although the framework does not directly involve a population sample, its potential to guide institutions in collecting and analyzing gender equity data is examined.Results: The Gender Equality Framework does not present quantitative results but offers practical guidelines to create fair environments in HEIs, prevent harassment, and reduce wage gaps.Additionally, it contributes to the development of open databases for STEM, fostering greater gender equity.Conclusions: The study highlights that
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".