The Ethical Tipping Points of Evaluators in Conflict Zones
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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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 | 0.212 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.066 |
| Scholarly communication | 0.030 | 0.023 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".