Bibliographic record
Abstract
To obtain the views of scholars on the so-called crisis of the scholarly monograph, a questionnaire was sent to 1,416 historians in doctoral/research universities asking about experiences in getting their books published and their opinions on a range of issues relating to publication. Included were questions on refereeing, on the changes they have encountered in the publication process since their first book, on electronic publishing, and on expectations of the future. Additional questions related to their practices as readers and buyers of books. Among the major conclusions were that the refereeing process is considered essential and that it accomplishes its purposes successfully; that there exists widespread reluctance to publish in a format that is available only electronically; that the emphasis on the bottom line in university presses has had an impact on the topics historians have chosen to investigate; that there is no agreement on the kind of books that history needs, although many would like to see more attention paid to what individuals who are interested in history but are not themselves scholars would like to read; and that the majority of historians are not finding it more difficult to get their books published than they did earlier in their careers.
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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".