Open-Access Monograph Publishing and the Origins of the Office of Digital Scholarly Publishing at Penn State University
Bibliographic record
Abstract
This essay explains the background of open-access monograph publishing as developed principally by university presses, often in association with libraries. It begins with discussions at Princeton University Press in the early 1970s about how to deal with the crisis of scholarly monograph publishing and moves on to describe a joint library/press project in the Committee on Institutional Cooperation (CIC) in the early 1990s. The failure of that project to be funded led the library and press at Penn State to launch a jointly operated Office of Digital Scholarly Publishing in 2005, which supported one of the pioneering programs in open-access monograph publishing. The CIC project, in particular, anticipated the proposal by the Association of American Universities / Association of Research Libraries, announced in June 2014, to subvent the publication of first monographs using an open-access model.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".