Achieving global open access: the need for scientific, epistemic and participatory openness
Bibliographic record
Abstract
Achieving Global Open Access explores some of the key conditions that are necessary to deliver global Open Access (OA) that is effective and equitable. Often assumed to be a self-evident good, OA has been subject to growing criticism for perpetuating global inequities and epistemic injustices. It has been seen as imposing exploitative business and publishing models and as exacerbating exclusionary research evaluation cultures and practices. Pinfield engages with these issues, recognising that the global OA debate is now not just about publishing business models and academic reward structures, but also about what constitutes valid and valuable knowledge, how we know, and who gets to say. The book argues that, for OA to deliver its potential, it first needs to be associated with ‘epistemic openness’, a wider and more inclusive understanding of what constitutes valid and valuable knowledge. It also needs to be accompanied by ‘participatory openness’, enabling contributions to knowledge from more diverse communities. Interacting with relevant theory and current practice, the book discusses the challenges in implementing these different forms of openness, the relationships between them, and their limits. Achieving Global Open Access is essential reading for academics and students engaged in the study of Library and Information Science, Open Access and Publishing. It will also be valuable and interesting to library and publishing professionals around the world.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.063 |
| Scholarly communication | 0.037 | 0.049 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".