Bibliographic record
Abstract
This paper is a critical sequel to John Dove’s paper titled “Maximum Dissemination: A possible model for society journals in the humanities and social sciences to support Open while retaining their subscription revenue”, presented at the Charleston Conference 2019. Dove’s OA advocacy has included both gold and green. Dove’s innovative model, which makes full use of the green route to achieve maximum dissemination of authors’ works through open repositories, suggests a switch in the functional responsibility for depositing author’s manuscript from author to publisher. The model has publishers to act as agents of the authors as much through the green route as their subscription route. Dove has suggested this maximum use of the green path by the publisher for specific journals in specific disciplines. This paper looks to examine the feasibility of green OA model in this context, and then to consider other ways to expand on this idea to other green OA supporting publishers. It further looks at the possibilities of the model driving the re-emergence of green OA as a favoured option for facilitating immediate and parallel dissemination of authors’ papers through both green and subscription channels.
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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.030 | 0.032 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".