Bibliographic record
Abstract
Abstract Civilian ceasefire and civilian protection monitoring are often seen as innovative peacekeeping and protection mechanisms in conflict zones difficult to access for international actors. However, the literature on civilian monitoring and its impact is sparse. In many conflicts, civilians organize to protect themselves. Research into civilian agency and protection has shown that civilian capacity to self-protect and conflict conditions determine whether protective civilian agency can be effective. We analyze whether civilian protection monitoring can positively impact the protection of civilians, focusing on Myanmar, where donors have funded civilian ceasefire monitoring efforts that are inclusive of a strong civilian protection component. We argue that despite failed ceasefires in Myanmar, the nurturing of civilian monitoring networks, that is, supporting civilian capacity, had a positive—albeit limited—impact on civilian protection. Monitors adapted knowledge from international ceasefire monitoring trainings to their reality on the ground and implemented civilian protection monitoring. Yet, conflict conditions seriously limited protection monitoring and posed grave security challenges to monitors and communities. We conclude that in conflict situations where armed actors show little sensitivity to civilian preferences and commitment to respecting human rights, the need for civilian protection is high while the protective potential of civilian monitoring is limited as long as armed groups’ incentives to better protect civilians remain weak.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".