Ten-Year Cross-Disciplinary Comparison of the Growth of Open Access and How it Increases Research Citation Impact
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
Lawrence (2001)found computer science articles that were openly accessible (OA) on the Web were cited more. We replicated this in physics. We tested 1,307,038 articles published across 12 years (1992-2003) in 10 disciplines (Biology, Psychology, Sociology, Health, Political Science, Economics, Education, Law, Business, Management). A robot trawls the Web for full-texts using reference metadata ISI citation data (signal detectability d'=2.45; bias = 0.52). Percentage OA (relative to total OA + NOA) articles varies from 5%-16% (depending on discipline, year and country) and is slowly climbing annually (correlation r=.76, sample size N=12, probability p < 0.005). Comparing OA and NOA articles in the same journal/year, OA articles have consistently more citations, the advantage varying from 36%-172% by discipline and year. Comparing articles within six citation ranges (0, 1, 2-3, 4-7, 8-15, 16+ citations), the annual percentage of OA articles is growing significantly faster than NOA within every citation range (r > .90, N=12, p < .0005) and the effect is greater with the more highly cited articles (r = .98, N=6, p < .005). Causality cannot be determined from these data, but our prior finding of a similar pattern in physics, where percent OA is much higher (and even approaches 100% in some subfields), makes it unlikely that the OA citation advantage is merely or mostly a self-selection bias (for making only one's better articles OA). Further research will analyze the effect's timing, causal components and relation to other variables.
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 | 0.034 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.030 | 0.098 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.014 | 0.037 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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