Bibliographic record
Abstract
In 2010, the Canadian government introduced the National Action Plan for the Implementation of UN Security Council Resolutions on Women, Peace and Security. Approximately 24 countries have developed national action plans to evaluate and monitor the implementation of UNSCR 1325 that calls for the inclusion of all women in peacemaking, peacekeeping, and peacebuilding and the protection of women. Refugee women were not included in the Action Plan as partners in peacemaking, mentioned only in sections referring to protection and post-conflict reconstruction. As such, refugee women are not considered key players in plans to bring about peace despite evidence that refugee women's organizations can participate in and even lead peacebuilding efforts.This chapter analyzes the activities of three refugee women's organizations from Tibet, the Sudan, and Burma/Myanmar concluding that it is strategically important to support women's transnational networks and facilitate contact between diaspora, refugee, and local women's organizations interested in conflict transformation. A gendered analysis of refugee peacebuilding capacity reveals gaps in peacebuilding capacity approaches that become evident when female diasporas are the focus of the research. The women's refugee organizations show the capacity for transnational bridge building, that is, the capacity to build and sustain networks across geographical, social and political boundaries with the aim of bringing about nonviolent social change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".