Journal editing and ethical research practice: perspectives of journal editors
Bibliographic record
Abstract
This article offers perspectives from academics with recent journal editing experience on a range of ethical issues and dilemmas that regularly pose challenges for those in editorial roles. Each contributing author has provided commentary and reflection on a select topic that was identified in the research literature concerning academic publishing and journal editing. Topics discussed include the ethical responsibilities of working with international and early career contributors to develop work for publication, balancing influence and responsibility to a journal's disciplinary field while maintaining the integrity of editorial and review processes, and the challenges of promoting scholarly research that pushes epistemological, methodological, and political boundaries in an increasingly competitive publishing climate. This article aims to stimulate discussion concerning the roles, responsibilities, and ethical challenges faced by journal editors, and the implications of these for ethical practices in academic publishing today.
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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 | 0.225 | 0.430 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.042 | 0.047 |
| Scholarly communication | 0.074 | 0.026 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.023 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 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".