Shifting the Power: Opportunities for Innovative Partnerships with Womens Movements
Bibliographic record
Abstract
The launch of Canada's Feminist International Assistance Policy in June 2017 included an allocation of $150 million over five years to the Women's Voice and Leadership Program, signaling a commitment to work closely with local women's organizations to advance the Sustainable Development Goals. This presents an extraordinary opportunity for Canada to show global leadership. To explore practical and creative ways of delivering on this commitment, The MATCH International Women's Fund, the Nobel Women's Initiative, and Global Affairs Canada hosted a panel and workshop that tapped the experience and wisdom of international experts, human rights activists, government officials, and civil society partners. This report has been prepared by the Nobel Women's Initiative and The MATCH Fund as a summary of this two-day event.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.024 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 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".