From Knowledge to Action: Using Sex, Gender and Health Research to Improve Policy and Practice
Bibliographic record
Abstract
Innovations in Gender, Sex, and Health Research will showcase excellence and innovations across all domains of gender, sex, and health research, including but not limited to biomedical, clinical, health services and policy, population and public health, and social science research. The conference will highlight how accounting for gender and sex leads to improvements in health interventions, policies, and outcomes. It will explore advances and challenges related to ethics and knowledge translation in gender, sex, and health research. The conference will bring together a multidisciplinary group of researchers with a shared interest in gender, sex, and health. It aims to promote networking and collaborative engagement among these researchers, as well as health care providers, policy-makers, community groups, and others with an interest in this topic. Innovations in Gender, Sex, and Health Research will foster training and educational opportunities for emerging gender, sex, and health researchers. Part of Keynote Webcast Series from the Innovations in Gender, Sex, and Health Research Conference (2010 : Toronto, ON).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.275 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.020 | 0.136 |
| Scholarly communication | 0.048 | 0.060 |
| Open science | 0.006 | 0.044 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".