Bibliographic record
Abstract
Abstract This chapter examines the “success stories” of the WPS agenda, interrogating how the agenda emerges as a triumph of transnational advocacy, a step forward in the seemingly endless search for strategies to mitigate against gendered inequalities and discrimination, and the prompt for—or ally of—related policy initiatives such as the UK’s Preventing Sexual Violence Initiative or the “feminist foreign policy” commitments of Sweden and Canada. Multiple articulations of success feature in the narrative of the agenda; for the purpose of identifying the fabula, I have organized these into two primary dimensions. First, the narration of the WPS agenda frequently cites the agenda itself as a success. The second dimension of the success story is the narration of moderate successes in implementation of the WPS agenda. These are the moments of change and, by implication, improvement to organizational structure or individual experience that the agenda has brought about. Over time, the ways in which these victories are presented, particularly in the Secretary-General’s reports but also in the contributions to and statements at Security Council Open Debates and even in interview data, rely more and more on quantitative data. Further, in terms of subject specificity, these successes are related increasingly to the prevention of sexual violence and women’s participation in peace processes, while other dimensions of the agenda are less well attended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".