Effects of open access publishing on article metrics in Neuropsychopharmacology
Bibliographic record
Abstract
Neuropsychopharmacology (NPP) offers the option to publish articles in different tiers of an open access (OA) publishing system: Green, Bronze, or Hybrid. Green articles follow a standard access (SA) subscription model, in which readers must pay a subscription fee to access article content on the publisher's website. Bronze articles are selected at the publisher's discretion and offer free availability to readers at the same article processing charge (APC) as Green articles. Hybrid articles are fully OA, but authors pay an increased APC to ensure public access. Here, we aimed to determine whether publishing tier affect the impact and reach of scientific articles in NPP. A sample of 6000 articles published between 2001-2021 were chosen for the analysis. Articles were separated by article type and publication year. Citation counts and Altmetric scores were compared between the three tiers. Bronze articles received significantly more citations than Green and Hybrid articles overall. However, when analyzed by year, Bronze and Hybrid articles received comparable citation counts within the past decade. Altmetric scores were comparable between all tiers, although this effect varied by year. Our findings indicate that free availability of article content on the publisher's website is associated with an increase in citations of NPP articles but may only provide a moderate boost in Altmetric score. Overall, our results suggest that easily accessible article content is most often cited by readers, but that the higher APCs of Hybrid tier publishing may not guarantee increased scholarly or social impact.
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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 | 0.045 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".