One size does not fit all : organizational diversity in New Zealand tertiary sector ethics committees
Bibliographic record
Abstract
Since 1988 in NZ all university and funded health researchers have been mandated to seek ethical review for research projects \nAt the time, the Ministry of Health ethics committees were guided by an operational standard for health research, yet no equivalent national ethics statement has been produced to guide all University research in NZ (unlike the situation in Canada and Australia) \nAcademics are justifiably questioning of institutional efforts to temper their autonomy unnecessarily, but little is known – outside of local/individual experiences – about how ethics committees actually work \n \nThis current project seeks to identify strengths of alternative approaches in particular institutional circumstances. It maintains a critical edge centred on improving appropriate access to committee processes and deliberations, and on improving the potential ‘educative’ (vs. governance) focus of ethics committees. \n \n \nSome key findings: \nNo two committees share even broadly similar organizational structures. Four of the five committees are centralised, but the ways in which they operate differ significantly \n \nResearchers have a variable range of access to advice and consultation, and they tend not to use the optional provisions that exist \nAll five committees are involved in facilitating (varying) learning opportunities within committees and/or in exchanges with others
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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 | 0.094 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".