Problems and development strategies for research ethics committees in China’s higher education institutions
Bibliographic record
Abstract
The establishment of research ethics committees (REC) in China's higher education institutions (HEI) is lagging far behind western developed countries. This has at least partly directly led to anomie in scientific research ethics, as seen in the recent controversies involving a proposed human head transplant and gene-edited babies. At present, the problems for REC in China's HEI include lack of regulation, informal ethics reviews, lack of supervision and insufficient ethics review capacity. To counteract these problems, suggested measures include mandatory formation of formal ethics committee, administrative support from HEI, ethics approval letter prior to funding application, formulation of regulations and standard operating procedures, selecting and training for members and independent consultants, training for secretaries and staff, ethics training for investigators, and learning from the experience of HEI outside of China, such as the USA and Canada. The establishment of REC in China's HEI will greatly enhance the overall quality of ethics reviews in China. In addition to better protecting the rights and welfare of human participants, it is also conducive to maintaining the reputation of China's HEI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.302 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.012 | 0.014 |
| 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; 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".