Sustaining Scholarly Publishing: New Business Models for University Presses
Bibliographic record
Abstract
Within the scholarly communications ecosystem, scholarly publishers are a keystone species. University presses—as well as academic societies, research institutions, and other scholarly publishers—strive to fulfil our mission of ‘making public the fruits of scholarly research’ as effectively as possible within that ecosystem. While that mission has remained constant, in recent years the landscape in which we carry out this mission has altered dramatically. The expertise residing within university presses can help the scholarly enterprise prosper in both influence and impact as it moves ever more fully digital. However, the simple product-sales models of the twentieth century, devised when information was scarce and expensive, are clearly inappropriate for the twenty-first-century scholarly ecosystem. This report (a) identifies elements of the current scholarly publishing systems that are worth protecting and retaining throughout this and future periods of transition; (b) explores business models of existing projects that hold promise; (c) outlines the characteristics of effective business models; (d) addresses the challenges of the transitional period we are entering; and (e) arrives at recommendations that might allow us to sustain high-quality scholarship at a time when the fundamental expectations of publishing are changing.
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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.054 | 0.047 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".