Rethinking where and how we publish in health sciences
Bibliographic record
Abstract
Over the past few decades, scientific publishing has undergone significant transformations evolving from a print-based system to a digitised and globally accessible ecosystem.While this shift has facilitated faster dissemination and broader access to knowledge, it has also exposed systemic weaknesses, including the profiteering by major commercial publishers and persistent inequities in the publishing landscape.This opinion article aims to educate researchers in rehabilitation sciences and the broader health sciences who are unfamiliar with scholarly publishing models and practices, with the goal of fostering more accessible, equitable, and sustainable knowledge production and dissemination.We critically examine the limitations of traditional subscription models, as well as pay-to-publish open access (gold with article processing fees) and hybrid models, highlighting their financial and systemic barriers.In contrast, we advocate for more equitable alternatives: the free-to-readers and free-to-authors model (diamond open access), which typically involves publishing costs covered by academic institutions or public funders, and self-archiving (green open access).We also discuss the increasing importance of preprints and peer-reviewed preprints (peer-print articles) in decoupling knowledge dissemination from conventional journal publication.We argue for greater recognition of these latter models in academic evaluation and for institutional support of open infrastructures.We recommend broader reforms, including replacing authorship with contributorship, shifting the focus from novelty to reproducibility and transparency, and eliminating the journal impact factor as a criterion for evaluation.Collectively, these recommendations aim to reinforce a scholarly publishing ecosystem that prioritises equity, rigour, and the collective advancement of science.
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 | 0.282 | 0.421 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.019 | 0.089 |
| Scholarly communication | 0.123 | 0.083 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.023 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".