Open Access effect on uncitedness: A large-scale study controlling by\n discipline, source type and visibility
Bibliographic record
Abstract
There are many factors that affect the probability of being uncited during the first years after publication. In this study, we analyze three of these factors for journals, conference proceedings and book series: the field (in 316 subject categories of the Scopus database), the access modality (open access vs. paywalled), and the visibility of the source (through the percentile of the average impact in the subject category). We quantify the effect of these factors on the probability of being uncited. This probability is measured through the percentage of uncited documents in the serial sources of the Scopus database at about two years after publication. As a main result, we do not find any strong correlation between open access and uncitedness. Within the group of most cited journals (Q1 and top 10%), open access journals generally have somewhat lower uncited rates. However, in the intermediate quartiles (Q2 and Q3) almost no differences are observed, while for Q4 the uncited rate is again somewhat lower in the case of the OA group. This is important because it provides new evidence in the debate about open access citation advantage.
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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".