Knowledge Unlatched: A Global Library Consortium Model for Funding Open Access Scholarly Books
Bibliographic record
Abstract
Abstract This special issue of Cultural Science Journal is devoted to the report of a groundbreaking experiment in re-coordinating global markets for specialist scholarly books and enabling the knowledge commons: the Knowledge Unlatched proof-of-concept pilot. The pilot took place between January 2012 and September 2014. It involved libraries, publishers, authors, readers and research funders in the process of developing and testing a global library consortium model for supporting Open Access books. The experiment established that authors, librarians, publishers and research funding agencies can work together in powerful new ways to enable open access; that doing so is cost effective; and that a global library consortium model has the potential dramatically to widen access to the knowledge and ideas contained in book-length scholarly works.
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 | 0.061 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 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".