Negotiating Responsibility for Navigating Ethical Issues in Qualitative Research: A Review of Miller, Birch, Mauthner, and Jessop’s (2012) Ethics in Qualitative Research, Second Edition
Bibliographic record
Abstract
Ethics in Qualitative Research (Miller, Birch Mauthner, & Jessop, 2012), now in its second edition, uses a feminist framework to present a variety of issues pertinent to qualitative researchers. Topics include traditional challenges for qualitative researchers (e.g., access to potential participants, informed consent, overlapping roles), as well as those that have garnered more attention in recent years, particularly with regard to uses and consequences of technological advances in research. The book is critical of committees whose function it is to review proposed research and grant research ethics approval (e.g., University Research Ethics Committees [URECs], Research Ethics Boards [REBs], and Institutional Review Boards [IRBs]). The authors of this book are situated within the United Kingdom. The editors take the position that ethics oversight by the researchers themselves is preferable and that such boards and committees are not well equipped to review qualitative research. A rebuttal to this position is presented within this review. Ethics in Qualitative Research provides a good overview of ethical issues that researchers face and is effective in merging theory with practice. It would be strengthened by avoiding the debate over URECs or by offering concrete suggestions for how URECs can improve their reviews of qualitative research.
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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 | 0.062 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".