Where boys don’t dance, but women still thrive: using a development approach as a means of reconciling the right to health with the legitimization of cultural practices
Bibliographic record
Abstract
Human rights language has become a common method of internationally denouncing violent, discriminatory or otherwise harmful practices, notably by framing them as reprehensible violations of those fundamental rights we obtain by virtue of being human. While often effective, such women's rights discourse becomes delicate when used to challenge practices, which are of important cultural significance to the communities in which they are practiced. This paper analyses human rights language to challenge the gender disparity in access to health care and in overall health outcomes in certain countries where such disparities are influenced by important cultural values and practices. This paper will provide selected examples of machismo and marianismo discourses in certain Latin American countries on the one hand and of female genital cutting/excision (FGC/E) in practicing countries, both of which exposed to women's rights language, notably for causing violations of women's right to health. In essence, a reflective exercise is provided here with the argument that framing such discourses and practices as women's rights violations. Calling for their abandonment have shown that it may not only be ineffective nor at times appropriate, it also risks delegitimizing associated discourses, norms and practices thereby enhancing criticisms of the women's rights movement rather than adopting its principles. A sensitive community-based collaborative approach aimed at understanding and building cultural discourses to one, which promotes women's capabilities and health, is proposed as a more effective means at bridging cultural and gender gaps.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.049 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".