Transformative choices towards a sustainable academic publishing system
Bibliographic record
Abstract
Communicating new scientific discoveries is key to human progress. Yet, this endeavor has been increasingly hindered by monetary restrictions that restrain scientists from publishing their findings and accessing other scientists’ reports. This process is further exacerbated by a large portion of publishing media owned by private companies that, in contrast with journals from scientific societies, do not reinject academic publishing benefits into the scientific community. As the academic world is not exempt from economic crises and funding restrictions, new alternatives are necessary to support a fair and economically sustainable publishing system for scientists and society as a whole. After summarizing major shortcomings of academic publishing today, we present several solutions that span the levels of the individual scientist, the scientific community, and the publisher to initiate a transformative change towards more sustainable scientific publishing. By providing a voice to the many scientists who are fundamental protagonists, yet often powerless witnesses, of the academic publishing system, as well as a roadmap for implementing solutions, we hope this initiative will go beyond sparking increased awareness and promote a shift towards more sustainable scientific publishing practices.
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 | 0.098 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.040 | 0.036 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".