GBA: Beyond the Red Queen Syndrome
Bibliographic record
Abstract
Thank you for the invitation to participate in this important panel. I want to spend the time I have to explore two key issues related to the symposium theme of using gender based analysis as a vehicle for building knowledge for effective policy, programs, research and laws: first, the continuing need for gender-based analysis, and second, the imperative that such analyses be based on appropriate evidence. Because my own research is on the sociology of health and health care systems, and women’s health, I will be drawing on examples from these areas. For those familiar with the story Alice Through the Looking Glass, you will have guessed from the title of my presentation that I’ve decided to discuss these issues with the assistance of Lewis Carroll. So, let me begin by both thanking him and apologizing to him for using his words in ways that he most definitely never imagined. As all of you will know, the call for gender based analysis (GBA) is not a new one. Indeed, CIDA pioneered the concept of GBA in the mid-1970s (Williams 1999). In the 1980s and 1990s, the government of Canada embraced the principles of GBA, and by 1995, it adopted a policy requiring federal departments and agencies to use GBA to
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".