Should Acknowledgments in Published Academic Articles Include Gratitude for Reviewers Who Reviewed for Journals that Rejected Those Articles?
Bibliographic record
Abstract
Abstract It is a common practice for authors of an academic work to thank the anonymous reviewers at the journal that is publishing it. Allegedly, scholars thank the reviewers because their comments improved the paper and thanking them is a proper way to show gratitude to them. Yet often, a paper that is eventually accepted by one journal is first rejected by other journals, and even though those journals' reviewers also supply comments that improve the quality of the work, those reviewers are not customarily thanked. We contacted prominent scholars in bioethics and philosophy of medicine and asked whether thanking such reviewers would be a welcome trend. Having received responses from 107 scholars, we discuss the suggested proposal in light of both philosophical argument and the results of this survey. We argue that when an author's work is published, the author should thank the reviewers whose comments improved the paper regardless of whether those reviewers' journals rejected or accepted the work. That is because scholars should show gratitude to those who deserve it, and those whose comments improved the paper deserve gratitude. We also consider objections against this practice raised by scholars and show why they are not entirely persuasive.
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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 | 0.165 | 0.688 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".