The Ethical Tipping Points of Evaluators in Conflict Zones
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
What is different about the conduct of evaluations in conflict zones compared to nonconflict zones—and how do these differences affect (if at all) the ethical calculations and behavior of evaluators? When are ethical issues too risky, or too uncertain, for evaluators to accept—or to continue—an evaluation? These are the core questions guiding this article. The first section considers how the particularities of conflict zones affect our ability to conduct evaluations. The second section undertakes a selective review of the literature to better understand how ethical issues have been addressed both in evaluation research and in evaluation manuals. The third section draws on a series of structured conversations with evaluators to probe more deeply into the ethical challenges they face in conflict zones—with a particular interest in the “ethical tipping points” of evaluators. The fourth section considers ways evaluation actors can manage ethical challenges in conflict zones, concluding with a brief discussion of how these issues might be located more centrally in evaluation research and practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it