Security Sector Reform and the Paradoxical Tension between Local Ownership and Gender Equality
Bibliographic record
Abstract
This article analyses the tension or conflict that can exist between the principles of local ownership and gender equality that guide Security Sector Reform (SSR) programmes when gender discrimination and patriarchal values characterise the local environment (and ‘locals’ do not value gender equality). In these situations, international actors may be reluctant to advocate gender equality, regarding it as imposing culturally alien values and potentially destabilising to the SSR process. It is argued, however, that the tension between local ownership and gender equality is deceptive and merely serves to protect the power of dominant groups and disempower the marginalised, often serving to disguise the power relations at play in post-conflict environments and avoid addressing the security needs of those who are often at most risk. The paper concludes that rather than a tension existing between the two principles, in fact, local ownership without gender equality is meaningless. Moreover, failing to promote gender equality undermines the extent to which SSR programmes result in security and justice sector institutions that are representative of and responsive to the needs of both men and women. It can also perpetuate structural inequalities and conflict dynamics and, ultimately, limit the success of SSR and broader peacebuilding processes.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".