Bibliographic record
Abstract
Open-access (OA) publishing is on the rise [1]. Most cardiovascular journals provide OA avenues: 61% are hybrid journals, whereas 37% are fully OA [2]. In cardiology and cardiac surgery, article processing charges (APCs) cost a median of US$3000 (IQR: US$2500–3425) for 139 journals with APCs [2]. Hybrid journals are particularly costly, charging a median of US$3250 (IQR: US$3000–3500); only one-third of journals have discounts or waivers, thus primarily benefiting researchers capable of paying or fortunate enough to obtain waivers. Profit margins of up to 20–30% have created a multi-billion-dollar industry, wherein a handful of publishers dominate the market [3]. Unsurprisingly, an increasing number of predatory journals now seek to exploit unknowing researchers and researchers seeing no other way out [4]. The logical question, then, appears: ‘Is there an excess of OA?’ [1] Ethically, we have to balance situations wherein everyone is worse off (no or limited OA) with those wherein everyone is better off (OA for all). Simulating a plutocratic society, current APC trends create an undue division between those better off and those worse off. By contrast, a just society ensures equality of opportunity, wherein inequalities are only acceptable if they benefit those who are initially worse off [5]. The latter should be emulated in academic medicine. As clinicians, we aim to provide the best possible care according to the best available evidence. As researchers, our goal is to advance science and increase the reach of our findings. Why, then, should publishing be different?
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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.057 | 0.060 |
| 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".