Negotiating Racism u~-Style BY ELIZABETH PHILIPOSE
Bibliographic record
Abstract
Cet article nous parle de ces We can Canadiennes travaiflant pour f ygafiti, qui ont pu participer au view those processus de Beijing+S. L huteure commitments as metlhccentsurlesavantages,!vidents de travaiffer internationafement h bench ma r k ~ to ~ ' ~ a a f i t ~ des fimmes et elfe halue which we hold Beijing+S selon ce que les gouvernements ont accept,! ddi'm-OU governments p h t e r pour fivoriser l2galitt des accountable and fimmesIttraversLemondt. from which From Beijing to Beijing + 5 we push them further. Five years ago, the UnitedNations held the Fourth World Confer-ence on Women in Beijing in which governments produced a consensus document known as "The Platform forActionn (PFA) to implement women's equality globally. Attended by close to 30,000 representatives of non-governmental organizations (NGOS) and several dozen heads of state, the Beijing Conference was one in a series, following on previous international women's conferences (Mexico, 1975; Copenhagen,l980; Nairobi, 1985). It was hoped that despite the various dificulties experienced during the negotiations, the Beijing Platform for Action could be a strong commitment needed from states to move the international women's equality agenda forward. Five years later, the United Nations held the f i ~ e- ~ e a r review of the Beijing Platform for Action, (known as "Beijing+Sn), culminating in the twenty-third special session of the General Assembly, (UNGASS), entitled
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".