Bibliographic record
Abstract
When I heard about the seminar titled "Transnationalization of Solidarities and Women's Movements, " which took place in April 2005 (and from which this book originated), I wanted to be there to explore with academics why so many women and groups were -and continue to be -attracted to our global action.Having been the coordinator of the International Secretariat of the World March of Women during its first nine years of existence gave me a multitude of opportunities to learn from other women and reinforced my belief that feminist and women's movements are both a tool and a process that we must renew and keep alive.The process of renewal is especially important when we consider the contemporary global reality of a delocalizing world and economy.Listening to and reading over the presentations given in that seminar sparked some thoughts that I wanted to share with you as we participate in a collective effort to understand the impact of global transformations and to articulate the kind of social change that we are striving for in our lives and in our communities.The World March of Women is a global feminist movement that was conceived in 1995, when feminists in Quebec were organizing the Bread and Roses women's march against poverty.I had the privilege of coordinating this march and was also one of the women who thought that we should link our action with women's and feminist groups in other countries.Subsequently, we began spreading the idea of a global feminist march.Although
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.491 | 0.289 |
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".