The power of horizontal collaboration: an interoperable and open not-for-profit ecosystem for open access books
Bibliographic record
Abstract
Within the context of Open Science, book publishing has traditionally lagged behind in the adoption of open access, open data and corresponding practices. A number of not-for-profit infrastructures including the Directory of Open Access Books (DOAB), OAPEN, Open Book Collective (OBC), Public Knowledge Project (PKP), and Thoth Open Metadata have joined forces to collaboratively develop open, community-led solutions to the many barriers faced by publishers when considering the creation, discovery, distribution, archiving and financing of open access books. PKP offers an open source book production and title management system, Open Monograph Press (OMP), which is now being integrated with both Thoth and DOAB/OAPEN to provide robust, free and open metadata management and dissemination workflows for books and chapters. Interoperability between the collaborating infrastructures means publishers can be supported (by Thoth and OAPEN) in the distribution and archiving of OA book content and metadata to a variety of channels (including Crossref, the Internet Archive and Portico); have their content and metadata hosted in internationally recognised discovery and hosting solutions (including DOAB and OAPEN); access privacy-respecting usage metrics across multiple platforms (via the OPERAS Metrics service and COKI); and in creating and managing collective funding channels for OA books via the Opening the Future programme, and OBC – two models supporting presses to transition away from a reliance on author-facing fees through collective funding, with OBC also providing collective funding for open infrastructure providers. All in all, this close collaboration between like-minded infrastructures – and with some of them (OAPEN & PKP) having existed for more than a decade – constitutes a collective, community-led open, equitable ecosystem of interoperable not-for-profit services and platforms that are jointly active in a variety of international networks and communities, including the Copim community, OPERAS, and the Barcelona Declaration group of signatories and supporters.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.034 | 0.047 |
| Open science | 0.003 | 0.043 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".