Making OA monographs happen: Library-Press collaboration at the University of Ottawa, Canada
Bibliographic record
Abstract
At the University of Ottawa, Canada, the UO Press and the UO Library have developed a strategic partnership to publish and disseminate selected new monographs as gold open access (OA). Starting in 2013, the Library agreed to fund three books at C$10,000 per book (a total of C$30,000 per year) in order to remove barriers to accessing scholarship and to align with scholarly communication goals of the University. In 2015 this agreement was renewed for another three years and the funding was increased to cover four books (a total of C$40,000 per year). Ten titles have so far been published under this model. The data reveals that there have been 12,629 downloads as well as 16,584 page views of these titles, as of September 2015. There have been over 4,700 copies (print and EPUB) sold in spite of the free availability of the PDF version. This program has been very successful in terms of increasing the visibility and impact of the Press’s publications; in providing unrestricted access to new scholarly research; and also in providing a significant source of revenue for the Press. The goals, process and outcomes are described in the context of the UO Press and the UO Library.
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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.039 | 0.010 |
| Scholarly communication | 0.037 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 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".