Localising Women, Peace, and Security: community agency and ownership beyond national policies
Bibliographic record
Abstract
This article examines the localisation of the Women, Peace, and Security (WPS) agenda as a contested political process shaped by negotiations between local, national, and international actors. Drawing on semi-structured interviews with activists and women peacebuilders in 10 conflict-affected contexts, alongside analysis of policy documents, it explores how localisation is understood, implemented, and experienced at the community level. Findings reveal that while localisation can amplify community agency and embed WPS commitments in subnational governance, it is often constrained by centralised decision-making, tokenistic consultation, resource precarity, and donor-driven priorities. Effective localisation emerges where sustained relationships, direct funding to grassroots actors, inclusive participation, and multi-level accountability converge. This article advances a justice-oriented conceptualisation of localisation, reframing it from a technical exercise to a transformative process of redistributing power. It argues for context-responsive strategies that prioritise community ownership and ensure WPS implementation reaches the sites where peace and security are most urgently contested.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".