Bibliographic record
Abstract
The three previous studies about the suggested retail prices (SRPs) and the new title output of new scholarly books (covering the years 1989–2000; 2000–8; and 2009–11) revealed certain well-defined patterns for books published by university presses and commercial scholarly publishers. During those years, the price for commercial scholarly published books exceeded the SRP for university presses in the humanities, the social sciences, and in the scientific, technical, and medical categories, and commercial presses annually released more than three times the number of scholarly books than university presses did in these major book categories. Were these new title output and pricing trends between 1989 and 2011 also evident in 2012–14? What pricing and marketing theories and practices did commercial scholarly publishers use to enable them to charge higher SRPs than university presses? In a period of economic uncertainty, when far too many university presses face fiscal questions from university trustees and administrators and several presses have been on the verge of closing in recent years, what strategies can university presses utilize to generate more revenues for their books and remain a vitally important part of the entire scholarly publishing ecosystem?
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".