Bibliographic record
Abstract
A key difference between collecting life stories and doing research interviews is the role of the interviewer. While training in oral history may focus on using standard scripts to take a life story, research interviews are motivated by specific questions that arise from particular historical projects and are often not primarily focused on the biography of the interviewee. Therefore, the research interview can be seen as being both less personal with regard to the personal life story of the interviewee and more personal with respect to the foregrounding of the specific interests of the interviewer. Soraya de Chadarevian has been one of the first historians of science to systematically reflect on this and other differences between life story interviews and research interviews. In this contribution, Lara Keuck, who has herself made use of interviews in her research, interrogates de Chadarevian on her approach to research interviews in her historical practice. They discuss how de Chadarevian's personal approach has developed and changed over the past three decades and reflect on the methodological implications that can be distilled from this experience.
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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 | 0.118 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.014 | 0.054 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".