Bibliographic record
Abstract
The author reviews three books written during the 1990s which rely on oral history to explore the lives of Japanese picture brides, refugees, and female Native elders. This article reveals how these three works succeed in using oral history to give voice to the individuals from these non-hegemonic groups that had been neglected by scholars in the past. It also explores some of the pitfalls that can arise when adoptingan oral history method that fails to recognize the role of the interviewer as well as incorporate any authorial interpretation into the work. The article calls for more transparency and greater balance when producing oral histories. RÉSUMÉ L’auteure commente trois ouvrages des années 1990 basés sur l’histoireorale et destinés à explorer la vie de Japonaises mariées par correspondance, deréfugiés et de vieilles autochtones. L’article montre comment ces trois livres ont réussià utiliser l’histoire orale pour donner la parole à des personnes faisant partie de groupes non dominants et qui ont été négligés par les chercheurs dans le passé. Il explore également quelques-uns des pièges dans lesquels on peut tomber en adoptant une méthode d’histoire orale qui ne reconnaît pas le rôle de l’interviewer et qui n’intègre pas d’interprétation solide dans le travail. Dès lors, ce texte appelle à une plus grande transparence et à un plus grand équilibre lors de la production d’histoires orales.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.191 | 0.092 |
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".