Alternative Models of Ethical Governance: The 2016 New Brunswick-Otago Declaration on Research Ethics
Bibliographic record
Abstract
The current model of ethical governance in research involving humans in the social sciences and humanities relies on prospective ethics review in ensuring that research in conducted ethically. One of its key features is distrust to researchers and their initiatives regardless of the subject matter, discipline, research methodology or settings, sources of funding, or researcher's experience. This article discusses the New Brunswick Declaration on Research Ethics adopted by the participants of the Ethics Rupture: Alternatives to Research-Ethics Review Summit in 2013. In particular, it provides background for the regulatory capture of the social sciences by the biomedical institutions of ethics review. It concludes by examining the limitations of the Declaration, and offers a set of principles for the development of the New Brunswick Declaration following its discussion at the Ethics in Practice: Tensions around Ethics Review and Maori Consultation Conference at the University of Otago in Dunedin in May 2015.
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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 | 0.244 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.077 |
| Scholarly communication | 0.031 | 0.018 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.037 | 0.040 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".