The ethics of being an editor–researcher
Bibliographic record
Abstract
Although only a few months old at press time, ChatGPT has already established itself as one of the biggest disruptors of historical conceptions of authorship, reality and trust. The research community will no doubt face increasing challenges as it attempts to deal with peer review, conflicts-of-interest and publishing ethics. Readers may know that the International Journal of Community Music is a Committee on Publication Ethics (COPE) member. COPE establishes ethical guidelines for the academic publishing. No doubt these will evolve in the face of emerging artificial intelligence technology. The existing guidelines are helpful but still leave many issues unaddressed, such as what researchers should do when it comes to publishing in a journal they edit. In addition to Kathleen Turner’s autoethnographic reflective essay about the challenges arising from the COVID-19 crisis on a university-based community music training programme and Anna McMichael’s study of composer/musicians involved with the annual classical Tyalgum Music Festival in regional Australia, Issue 16:1 features three articles authored or co-authored by the journal’s editors, who devised an in-house system to ensure the integrity of the double-blind peer review system. The issue concludes with a dedication to Janice Waldron (1957–2022), who passed away suddenly and unexpectedly in November 2022.
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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 | 0.253 | 0.480 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.069 |
| Scholarly communication | 0.046 | 0.030 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.033 | 0.042 |
| Insufficient payload (model declined to judge) | 0.006 | 0.011 |
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".