The current status of Open Access in biomedical field: the comparison of countries relating to the impact of national policies
Bibliographic record
Abstract
Abstract The purpose of the article is to show the current status of Open Access (OA) in biomedical field, and compare some countries such as the U.S., the U.K. and Japan in terms of the OA situation. There are controversies about the definition of OA. After examining the requirements about OA, we recognized OA as the situation in which researchers could read the full text of articles in unrestricted way. In order to investigate the current situation of OA, 4,756 articles were sampled randomly from articles published between January and September in 2005 and indexed in PubMed. The main results are as follows: 1) The rate of OA articles was 25%, and 75% of all the articles were available online including electronic subscription journal articles. 2) The means of OA was classified into five types. Among them, the rate of OA articles by “OA and Hybrid OA journals” was overwhelming (more than 70%), and that of PMC was 26.2%. The rates of OA articles by “institutional repositories” and “authors' personal sites” were considerably low (6.0% and 4.9% respectively). 3) When comparing the rates of OA articles by countries, Belgium ranked the first with 41.7%. The five countries indicated more than 30% in OA articles: Canada and India (38.7%), Brazil (36.4%), Australia (30.8%), and the U.S. (30.7%). Each country was different in the means of OA. 4) We explored the rates of OA for two groups; one group consists of articles published in journals with IF, and the other consists of articles published in journals without IF. The rate of OA for the group of articles in journals with IF is 20.6%, and that of articles in journals without IF is 30.8%.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".