Bibliographic record
Abstract
Although ethics as a field and topic of human inquiry has a long history, concerns about research ethics are more recent. This editorial is intended to frame the McGINN and BOSACKI contribution on educating future researchers in research ethics that appears in this FQS issue. I provide a brief historical context for recent developments regarding research ethics and provide a classification of practitioners, who, in one way or another, have to deal with ethical issues in research. I end with a call for a greater salience of discussions concerning issues of research ethics, which should contribute to evolve our scientific community. URN: urn:nbn:de:0114-fqs040275
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 | 0.236 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.013 | 0.076 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".