Acknowledgments through the prism of the ICMJE and ChatGPT
Bibliographic record
Abstract
The International Committee of Medical Journal Editors (ICMJE) guidelines are widely employed as an set of ethical standards for biomedical journals, and thus for biomedical researchers. In this paper, we revisit the topic of acknowledgements in academic papers, noting that the former serve as a lesser form of recognition relative to authorship. We note the possible existence of bias, such as a power imbalance due to a status imbalance, as well as the risk of “ghost” acknowledgements. To further ground our ideas, we turned to ChatGPT-4 for input, noting some curious and informative supplementary findings. Curiously, ChatGPT-4 offered a set of recommendations and guidance, comparable to those of the ICMJE.
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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 | 0.171 | 0.733 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.054 | 0.028 |
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".