Empathy and Authority in Oral Testimony: Feminist Debates, Multicultural Mandates, and Reassessing the Interviewer and her “Disagreeable” Subjects
Bibliographic record
Abstract
Les archives spécialisées dans l’histoire orale décrivent peu la vie de leurs propres chercheurs, et les spécialistes de l’histoire orale s’intéressent rarement aux difficultés d’interpréter les entrevues réalisées par autrui. Cet article s’inspire des mémoires détaillées de la professeure féministe Vijay Agnew, qui a enregistré les témoignages d’immigrantes sud-asiatiques au Canada pour constituer une archive de l’histoire ethnique du Toronto des années 1970 afin d’étudier la relation entre l’empathie et la lutte pour le pouvoir dans les témoignages oraux. En situant l’archive dans le discours alors naissant au Canada sur le multiculturalisme officiel, l’article montre comment s’y prenaient tant l’intervieweuse que l’interviewée pour faire leurs, combattre ou propager les discours postcoloniaux qui s’affrontaient durant cette période afin de révéler la façon dont les immigrantes de la classe moyenne réagissaient à l’étiquette d’ethnicité qui leur était accolée et au racisme accru dont elles faisaient l’objet au milieu des années 1970.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.040 | 0.067 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".