Are Economic Pressures on University Press Acquisitions Quietly Changing the Shape of the Scholarly Record?
Bibliographic record
Abstract
The monograph remains central to humanities and qualitative social science (HSS) research as the form most suitable for the long-form argument and, crucially, as foundational to the tenure process in these fields. University and other scholarly presses have played a vital role in supporting the publication of scholarly monographs where such narrow research is not seen as being as commercially viable as, for example, journals. While there appears to be an erosion of traditional revenue streams, new funding models are not yet recuperating costs for scholarly monographs. Library budgets continue to tighten, with new collection strategies taking hold, putting strain on monograph purchasing where libraries were central supporters of the form. We wanted to know what these economic pressures meant for the ways in which editors at university and other scholarly presses choose to acquire books. Recent research has addressed the impact of cooperative library purchasing, the role of American university presses in shaping the monograph, effects of new business models and approaches to access, and the costs of producing scholarly monographs. But there has been little exploration into editorial practices as a part of this larger ecosystem. This paper presents preliminary results from a pilot study exploring the connection between revenue, the economics of publishing scholarly monographs, and the behaviors and choices of acquisitions editors.
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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 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".