I Want a Life Story not a Life Sentence. Legal, Ethical and Human Rights Issues Related Recording, Transcribing and Archiving Oral History Interviews
Bibliographic record
Abstract
This paper explores legal, ethical and human rights issues of conducting oral \nhistory interviews and focuses on problematic factors related to depositing the \nresultant audiotapes and transcripts in archives. Methods of protecting those \nwho may be harmed in anyway by the tapes or transcripts being open to public \naccess are identified. The potential ethical and legal consequences for \nresearchers are explored. The interviews were part of an historical research \nstudy into the history of Nursing in the two West Yorkshire towns of Halifax \nand Huddersfield, United Kingdom (UK) between 1870-1960. The two \nmethodological approaches were analysis of the primary and secondary \ndocumentary archival sources, and oral history interviewing of a sample of \ntwenty-one retired nurses ranging from 65-97 years old representative of \nlocation and career experience to ensure a strategic purposive sample. The \nresultant audiotapes and transcripts will be stored in the archives of The \nUniversity of Huddersfield.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".