Bibliographic record
Abstract
This book has a different starting point from others that concentrate on qualitative research ethics; it focuses on how qualitative researchers experience ethical dilemmas in the field and how they resolve them, not on how ethics committees review qualitative research. Moral panic (Hoonaard, 2001; Fitzgerald, 2005), ethics creep (Haggerty, 2004), travelers and trolls (Pritchard, 2002) are common accounts social scientists have used to characterize their uneasy relationship with ethics committees (Institutional Review Boards in the USA, Research Ethics Boards in Canada, Human Research Ethics Committees in Australia, and Research Ethics Committees in the UK). Israel and Hay (2006, p. 1) story the relationship as one where “social scientists are angry and frustrated, their work is being constrained and distorted by regulators of ethical practice who do not necessarily understand social science research.” Although mindful of these critiques, my position on these questions has focused less on outward critiques and more on the ethical considerations of qualitative research itself. Additionally, for most of the past fifteen years I have served on ethics committees, mostly as chairperson, and recently I worked to establish a not-for-profit company operating a noninstitutional ethics committee. The New Zealand Ethics Committee reviews applications gratis from researchers in local and central government and NGOs along with community researchers who are routinely disenfranchised from formal ethical review. Ethics committees play an important role in protecting participants from harm, yet their ability to evaluate qualitative research is incomplete. Obscured from ethics committees and researchers alike are the ethical events that unfold in the field.
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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 | 0.092 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.066 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".