Legal, ethical and human-rights issues related to the storage of oral history interviews in archives.
Bibliographic record
Abstract
This paper provides some personal reflections that explore the legal, ethical and human rights issues of conducting oral history interviews with elderly retired nurses. The interviews are part of a research study into the history of nursing in the West Yorkshire towns of Halifax and Huddersfield, UK, between 1870-1960. The merit of this research is that it provides a unique account of the development of nursing and can enrich our understanding of the implications for present-day practice within the fast changing world of the 21st century. A literature review identified a 'gap in knowledge' of how and why local nursing developed. This study proposes to bridge this gap and provide an investigative account of the important issues for local nursing. The two methodological approaches are analysis of the primary and secondary documentary archival sources, and oral history interviewing of retired nurses. 'Word of mouth' or snowball sampling identified over 300 potential interviewees ranging from 65-97 years old. A final sample of 21 representative of location, age and career experience was selected to ensure a strategic purposive sample. The resultant audiotapes and transcripts will be stored in the university's archives. The main focus of the paper will be the legal, ethical and human rights issues of storing interviewees tapes/transcripts in archives. Reflections on these problems and attempts to overcome them have been provided. These are centred on the issue of whether to edit the tapes and/or transcripts. Arguments are provided for and against editing and potential practical solutions to some of the practical issues are identified. The main aim is to identify methods that will enable the protection of those who may be harmed in anyway by the tapes or transcripts been open to public access.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".