North-south collaboration on research and advocacy to reduce armed violence
Bibliographic record
Abstract
Armed violence is a global problem, yet disproportionately impacts certain nations. In recent years, approximately 90% of all direct conflict deaths occurred in low-income countries in Asia, Africa and Latin America. The situation for indirect deaths is even more disproportionate. Further, the South contains almost all countries that are considered to have active conflicts or are post-conflict, related in part to a shared history of colonialism and underdevelopment. Finally, advocates and academics in the South face greater personal risks than those in the North when speaking out against armed violence, particularly if it involves being critical of political forces. Despite this situation, much of the research and advocacy on armed violence originates from universities and organisations in the North. This is particularly the case when looking at funding, leadership and the initiation of projects. This paper will identify best practices of North-South collaborations on armed violence research, education and advocacy. Precedent exists within research, particularly the work of International Physicians for the Prevention of Nuclear War, the Red Cross and other large medical non-governmental organisations. Similarly, best-practice examples of advocacy can be explored that demonstrate effective North-South collaboration. Future work would benefit from a clear definition of partnership, developing broader networks and working towards a South-driven movement. Support should focus on improving Southern access to resources, as identified by these partners. In addition, Northern partners should prioritise examining and changing the role their societies play in sustaining armed violence in the South.
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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 | 0.091 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.046 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".