MétaCan
Menu
Back to cohort
Record W3134735527 · doi:10.1111/theo.12310

Should Acknowledgments in Published Academic Articles Include Gratitude for Reviewers Who Reviewed for Journals that Rejected Those Articles?

2021· article· en· W3134735527 on OpenAlexfundno aff
Joona Räsänen, Pekka Louhiala

Bibliographic record

VenueTheoria · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsnot available
FundersUniversitetet i OsloRyerson University
KeywordsGratitudeArgument (complex analysis)PublishingQuality (philosophy)PsychologySociologyEpistemologyLawPolitical scienceSocial psychologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.297
GPT teacher head0.402
Teacher spread0.105 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueTheoriaSame topicEpistemology, Ethics, and MetaphysicsFrench-language works237,207