Theory and key concepts in gender, sex, and health research
Bibliographic record
Abstract
Leading experts present a foundational introduction to the field of gender, sex, and health research. Participants will expand their understanding of: 1. The differences between sex and gender, while taking into account the complexities of each category (Dr. Joy Johnson); 2. How paradigms inform definitions and designs in gender, sex, and health research (Dr.Blye Frank); 3. The concept of intersectionality, and how other markers of social difference intersect with gender and sex (Dr. Olena Hankivsky); and 4. How gender and sex can be integrated in health research, as well as best practices and emerging innovations in the field (Dr. Gillian Einstein). Presenters: Dr. Joy Johnson, Scientific Director, CIHR Institute of Gender and Health; Dr. Blye Frank, Chair, Institute Advisory Board, Institute of Gender and Health; Professor and Head of Division of Medical Education, Dalhousie University; Dr. Olena Hankivsky, Associate Professor, Public Policy Program, Simon Fraser University; Dr. Gillian Einstein, Associate Professor, Departments of Psychology and Public Health Sciences, University of Toronto.
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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.077 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".