Community-Led Infrastructures for Open Access Books: A Sustainable Model and Platform
Bibliographic record
Abstract
This paper introduces two major outputs of the Community-led Open Publication Infrastructures for Monographs project, the Open Book Collective (OBC) and platform and the Opening the Future publishing model. The OBC, a charitable entity, will host an infrastructure and revenue management platform for the support, access, distribution, and promotion of open access (OA) books beyond models relying on book processing models. I then discuss a revenue model for publishers who wish to flip to an OA model without book processing charges. This ‘Opening the Future’ model has already been successfully implemented by two publishers, the Central European University Press and Liverpool University Press. This paper relates to the conference theme of sustaining positive change. The international move towards OA book publishing must be approached through models that render OA books equitable and accessible to the widest variety of international readers and authors. This necessitates thinking beyond book processing charges and the potential monopolisation of the OA landscape by major publishers, supporting a diversity of approaches in a networked model we call ‘scaling small.’
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".