Oral History in UK Doctoral Research: Extent of Use and Researcher Preparedness for Emotionally Demanding Work
Bibliographic record
Abstract
Oral history is increasingly used in academic teaching and research across many disciplines and contexts in the UK. However, there is currently no accurate picture of the extent to which oral history is practiced at the doctoral level and the diversity of its disciplinary and institutional contexts. Similarly, there is no clear understanding of how doctoral students are prepared for doing oral history research and what their particular concerns might be. This article presents the findings from a recent mixed-method pilot study which explored (1) the extent of use of oral history in doctoral research both as a main methodology and a supplementary method of data collection, and (2) the conceptual, ethical, and practical needs of doctoral students engaging with oral history. Focus group interviews generated detailed discussion of the often-unrecognized emotional labor involved in oral history research, the lack of preparedness in dealing with it, its potential impact on the researcher, and ways of mitigating this. This article examines the underinvestigated element of emotional labor in conducting oral history research, entanglements of responses and responsibilities, and ways of practicing an ethics of care in the current higher education context.
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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 | 0.115 | 0.323 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| 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".