Sex, gender, and health biotechnology: points to consider
Bibliographic record
Abstract
BACKGROUND: Reproductive technologies have been extensively debated in the literature. As well, feminist economists, environmentalists, and agriculturalists have generated substantial debate and literature on gender. However, the implications for women of health biotechnologies have received relatively less attention. Surprisingly, while gender based frameworks have been proposed in the context of public health policy, practice, health research, and epidemiological research, we could identify no systematic framework for gender analysis of health biotechnology in the developing world. DISCUSSION: We propose sex and gender considerations at five critical stages of health biotechnology research and development: priority setting; technology design; clinical trials; commercialization, and health services delivery. SUMMARY: Applying a systematic sex and gender framework to five key process stages of health biotechnology research and development could be a first step towards unlocking the opportunities of this promising science for women in the developing world.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".