Bibliographic record
Abstract
consortia, libraries and library consortia, and other knowledge institutions committed to working together across the landscape of the open knowledge commons to enable a more sustainable future for open-access (OA) book-length and long-form scholarship.What members of the OBC do varies, but what we share is a commitment to building an open knowledge-and resource-sharing ecosystem that will exemplify Our aim is to support the production and dissemination of OA books in a rich diversity of forms, while also being responsive to, as well as driven by, the community of communities 2 dedicated to public knowledge and the love of the book.Instead of dominant approaches to organisational growth that tend to flatten community diversity through economies of scale, almost always aimed at 'scaling up,' 'scaling small' is nurtured here through intentional collaborations between community-driven OA book publishers and open publishing service providers that promote a bibliodiverse ecosystem while also providing resilience through resource sharing and other kinds of collaboration. 3
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".