The Limits of Oral History: Ethics and Methodology Amid Highly Politicized Research Settings
Bibliographic record
Abstract
In recent years, oral history has been celebrated by its practitioners for its humanizing potential, and its ability to democratize history by bringing the narratives of people and communities typically absent in the archives into conversation with that of the political and intellectual elites who generally write history. And when dealing with the narratives of ordinary people living in conditions of social and political stability, the value of oral history is unquestionable. However, in recent years, oral historians have increasingly expanded their gaze to consider intimate accounts of extreme human experiences, such as narratives of survival and flight in response to mass atrocities. This shift in academic and practical interests begs the questions: Are there limits to oral historical methods and theory? And if so, what are these limits? This paper begins to address these questions by drawing upon fourteen months of fieldwork in Rwanda and Bosnia-Hercegovina, during which I conducted multiple life history interviews with approximately one hundred survivors, ex-combatants, and perpetrators of genocide and related mass atrocities. I argue that there are limits to the application of oral history, particularly when working amid highly politicized research settings.
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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 | 0.152 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".