Bibliographic record
Abstract
Acknowledgements are the one place on public record where scholarly editors receive credit for their contributions to books. Some have argued that the tradition of editorial invisibility is self-defeating; one editor has proposed that editors receive official recognition from the publisher in a book's front matter, irrespective of author acknowledgement. As a preliminary to entertaining such a proposal, this article surveys current practices by authors in a content analysis of acknowledgements sampled from monographs recently published by university presses. The analysis found that editors were often characterized as manuscript shepherds having a dual role: ally to the author and expeditor of production. Speaking for scholarly editors, I oppose making editor recognition a publishing standard, as this might suggest that authors need not credit editors on their own. The cost of such omission would be high, as editors would be left out of the back story to their books.
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 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 | 0.082 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".