Open access increases citations of papers in ecology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Open access ( OA ) can effectively increase the accessibility and visibility of scientific articles and thus potentially confer them with citation advantages. Such an impact may be more pronounced in developing countries where the cost for journal subscription is comparably expensive and usually unaffordable. By comparing one OA article with one non‐ OA article published in the same issue, we tested the impact of OA on citation advantages of articles published in 46 ecology journals indexed in the Journal Citation Reports (JCR). We compared OA to non‐ OA articles published in the same issue of these journals, thereby controlling for potentially confounding effects of publication requirement and period. OA articles received significantly more citations than non‐ OA articles, and this citation advantage of approximately one citation per year was sustained across publication years from 2009 to 2013. The OA citation advantage did not depend upon income of the country of origin of the citing scientists, and the OA citation advantage was found for citing scientists from North America, Europe, Asia, Africa, and Oceania, but not for Latin America. A total of 10 countries contributed more than 1000 citations each, and the OA citation advantage was found in all the 10 countries except Canada. Therefore, in ecology journals OA confers articles with citation advantages and such an impact accumulates with years and independent of the economic status of the countries. This information may guide decisions of scientific societies, journals, and individual authors as they weigh the relative costs and benefits of open electronic accessibility of scientific research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.027 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it