Can integrity issues encountered by a publisher inform best practices at institutions? Reflections from the World Conference on Research Integrity 2022.
Bibliographic record
Abstract
At the World Conference on Research Integrity in June 2022, we held a symposium session to discuss whether sharing information on research integrity and publishing ethics cases seen at a publisher could inform the training and support that researchers need from institutions. Here we reflect on the data and views presented and the discussion that followed. We recommend that all stakeholders involved in promoting research integrity pursue the following four goals to reshape research culture: adoption of a shared granular taxonomy that emphasises research quality; transparent reporting from publishers and institutions on the number and type of research integrity and publishing ethics cases seen annually; delivery of research integrity and publishing ethics training with emphasis on research quality; adoption of open research initiatives and the creation of healthy, inclusive and diverse work environments.
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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 | 0.252 | 0.518 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.025 | 0.038 |
| Scholarly communication | 0.072 | 0.069 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.038 | 0.057 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".