Bibliographic record
Abstract
European researchers and members of ethics committees feel the need to improve Good Scientific Practice, to ensure the protection of human subjects in clinical trials and to evaluate consequences of research. To enhance the current situation, a focus on the process of training and training materials in research ethics is desirable. Since the complicated moral issues of research, often divergent, cannot be addressed solely by an individual judgement based on a mere natural intuition. If the moral consciousness of the researcher is supposed to enable him to perceive and evaluate his actions adequately, special training is required to attain the perceptive sensitivity for possible conflicts and the conceptual clarity for possible solutions. Based on the reflection on the structure and items of research, ethics results of a European study are introduced and elements of a European core curriculum for teaching research ethics are proposed. The paper aims at reviewing and comparing the existing training materials on research ethics in Europe, the USA and Canada. It also proposes different modules of research ethics training for the members and secretariat of Research Ethics Committees. The paper emphasises the urgent need to establish a European curriculum in research.
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 | 0.184 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.074 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".