The ethics of being an editor–researcher
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it