Research ethics committees in the UK--the pressure is now on research and development departments
Bibliographic record
Abstract
Picture this. You have a research question of international importance. Against the odds, you have secured non-commercial funding for a well-designed multicentre project that will answer the question. Naturally, you must obtain research ethics committee (REC) approval. You apply to an REC online. After clarifying questions about your application on just one occasion, you get a decision at no charge, within 60 days, and it is valid for the whole of your country. While this scenario is a fantasy in countries such as the USA,1 Canada,2 and Australia,3 a recent Department of Health (DoH) report celebrates this new era of REC operation in the UK.4 But to what extent do the procedures in the UK meet this ideal, and have they improved? Do other regulatory hurdles lurk in the shadow cast by the mighty new REC system? In other words, is British researchers' long battle against red tape over?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.150 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.028 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".