Australian research ethics governance: Plotting the demise of the adversarial culture
Bibliographic record
Abstract
During the past few years, researchers have expressed serious concerns about the impact of the requirements for research ethics review on the nature of social science research in general and qualitative research in particular. Such fears have been raised repeatedly in countries with relatively lengthy histories of research ethics regulation, including Australia, the United States, and Canada. Discontent has also surfaced in the United Kingdom and Brazil as they have moved towards a more centralized response to research ethics and integrity. Social scientists complain that neither the writers of the codes that govern research nor the local ethics board members who review research projects understand their roles (Israel, 2015). As a result, researchers believe that important work, work that is both ethically and methodologically sound, is being blocked and even stigmatized by the research ethics bureaucracy. In some places, the end result is a system where regulators and regulated view each other as responsible for an increasingly antagonistic relationship. In short, we have seen the growth of elements of an adversarial culture in the regulation of research ethics.
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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 | 0.054 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.035 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".