Open access improves the dissemination of science: insights from Wikipedia
Bibliographic record
Abstract
Abstract Wikipedia is a well-known platform for disseminating knowledge, and scientific sources, such as journal articles, play a critical role in supporting its mission. The open access movement aims to make scientific knowledge openly available, and we might intuitively expect open access to help further Wikipedia’s mission. However, the extent of this relationship remains largely unknown. To fill this gap, we analyse a large dataset of citations from the English Wikipedia and model the role of open access in Wikipedia’s citation patterns. Our findings reveal that Wikipedia relies on open access articles at a higher overall rate (44.1%) compared to their availability in the Web of Science (23.6%) and OpenAlex (22.6%). Furthermore, both the accessibility (open access status) and academic impact (citation count) significantly increase the probability of an article being cited on Wikipedia. Specifically, open access articles are extensively and increasingly more cited in Wikipedia, as they show an approximately 64.7% higher likelihood of being cited in Wikipedia when compared to paywalled articles, after controlling for confounding factors. This open access citation effect is particularly strong for articles with high citation counts or published in recent years. Our findings highlight the pivotal role of open access in facilitating the dissemination of scientific knowledge, thereby increasing the likelihood of open access articles reaching a more diverse audience through platforms such as Wikipedia. Simultaneously, open access articles contribute to the reliability of Wikipedia as a source by affording editors timely access to novel results.
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 | 0.003 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".