Bibliographic record
Abstract
Open access to the peer-reviewed primary research literature would greatly facilitate knowledge transfer between the creators and the users of the results of research and scholarship. Criteria are needed to assess the impact of recent initiatives, such as the Budapest Open Access Initiative. For example, how many open-access research journals exist within a given field, and what is the reputation of each one? And, how many openly-accessible institutional e-print archives have been created and how many are actually are being used by researchers and scholars? A simple approach to an assessment of the open-access portion of the medical literature is described, and some preliminary results are summarized. These preliminary results point to the need for incentives to foster the implementation of initiatives such as the Budapest Open Access Initiative. An example of an incentive model is proposed, where an agency or foundation that provides peer-reviewed grants-in-aid to researchers establishes an e-print archive. Only current grantees of the agency would be eligible to post reports about the results of research projects or programs that have been supported by the agency. Some advantages and implications of this particular model are outlined. It is suggested that incentive models of this kind are needed to increase the likelihood that open access to the primary medical research literature will soon reach a "tipping point" and move quickly toward wide acceptance.
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 | 0.023 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".