A Bibliometric Analysis of the Literature on Open Access in Scopus
Bibliographic record
Abstract
Using bibliometric techniques, this study investigates the characteristics of the literature on open access related research. The bibliometric data collected from Scopus, such as document type, country of publication, language of publication, subject area and the publication year of the open access documents, is used. In addition, the most cited articles, the top journals, the most productive authors and the institutions with the highest number of papers are also identified. The results of the study show that: 1.thirteen document types and 7,721 documents from 1972 to 2012, peer-reviewed journal articles (4,793; 62%) are the most frequently used type and the most popular publication media; 2.the US (2,204; 27%) and the UK (1,172; 14%) with 3,376 (41%) of the articles, are the countries with the greatest contribution, from a total of 128 countries’ authors; 3.English (7,316; 94%) dominates the other languages as the most frequently used language; 4.the top 3 subject areas are medicine ( 2,753; 22％), social science (1,787; 14%), biochemistry and genetics, molecular biology (1,253; 10％); 5.the last 10 years (20032012) account for 6513 (84.3%), as the highest output; 6.the most cited papers, published on Remote Sensing of Environment, are cited 2043 times, written by 13 coauthors in 1998 and supported by NASA in the US; 7.Plos One, with the most total publications on open access, published 554 papers; 8.the top 3 most productive authors are Bjork, B.C., from Finland, with 29 articles on open access, McGrath, M., from the United Kingdom, with 27articles on open access and Harnad, S., from Canada, with 24articles; and 9.the top institution is the University of Toronto (Canada). The future development of open access research will be of increasing importance, with more subject areas, authors, institutions and journals. The OA movement, an innovation in scholarly communication, is growing quickly and will widely influence in different subject areas and changes in related research worldwide.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.292 | 0.337 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".