Mapping Gender and the Responsibility to Protect: Seeking Intersections, Finding Parallels
Bibliographic record
Abstract
Over the past decade, the international community has acknowledged that traditional notions of conflict and protection must be re-visited if true human security is to be realized. Consistent with this recognition, both the responsibility to protect and the women, peace, and security agenda challenge the status quo and offer new perspectives from which to approach responses to conflict. Unfortunately, the former was developed without consideration of the latter, and a tremendous opportunity to benefit from years of experience and expertise was thus missed. This article demonstrates that while recent discourse surrounding the responsibility to protect suggests some increased awareness that conflict affects men and women differently, there remains a significant disconnect between the development of this framework and the ever-growing body of work on the gendered nature of peace and security issues. Our identification of this ongoing chasm is accompanied by two simple observations: first, that this renders the responsibility to protect inconsistent with other international commitments and priorities; and second, that incorporation of the links between gender and conflict will improve the ability of the responsibility to protect to afford true protection.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".