Ethical Issues in Socio-Historical Archival Research: A Short Skit
Bibliographic record
Abstract
Qualitative methods associated with historical sociology have enjoyed a revival. Yet, the ethical issues raised by these methods have not been adequately explored. This paper fills this gap through a short skit performed by a researcher, a qualitative research methods textbook, and the Tri-Council Policy. The researcher in the skit is conducting archival research on the historical involvement of women in Canadian labour struggles. By employing a scripted narrative, this paper challenges conventional writing on method and ethics. Through this innovative approach many of the ethical issues and tensions that socio-historical researchers encounter in their research are captured. Questions about the ethics of socio-historical archival research, as well sociological research more generally, are raised.
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 | 0.087 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".