Sources of evidence and openness in field-intensive research on violent conflict
Bibliographic record
Abstract
This article engages with Steven Lubet’s arguments in Interrogating Ethnography on reliability of evidence and replication of findings in ethnographic research. It draws on eight months of immersive fieldwork on Abkhaz mobilization in the Georgian-Abkhaz war of 1992–1993 to show that field-intensive researchers who work on sensitive political topics leverage multiple sources to develop their insights and engage in reflexivity while prioritizing the safety of their research participants. It is these practices that underlie the trustworthiness of research and form the basis for the evaluation of research results rather than verification standards proposed by Lubet that do not, and cannot, apply to this kind of research.
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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 | 0.372 | 0.638 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.013 | 0.070 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.007 |
| 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".