Data-Driven Peacekeeping and the Vancouver Principles: Towards Improved Monitoring and Reporting for Grave Violations Against Children
Bibliographic record
Abstract
This article examines the UN’s move toward ‘data-driven’ peacekeeping and its implications for the Vancouver Principles, especially implementation of states’ monitoring and reporting commitments as outlined in Principle 6. I argue that data-driven peacekeeping presents both opportunities and challenges when it comes to monitoring and reporting. On the one hand, it can improve the quantity and quality of the information available about the recruitment and use of child soldiers. It can thereby foster improvements in responsiveness, performance, and accountability, both within peace operations and among other stakeholders. Yet data-driven peacekeeping also comes with challenges. These include data literacy and ‘buy-in’ among personnel on the ground, concerns about privacy and confidentiality, and political sensitivities around monitoring and reporting. Together these issues highlight the degree to which the Vancouver Principles are interconnected and mutually reinforcing – each affects implementation of the others, and none can be fully operationalized in isolation.
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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 | 0.099 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".