Publons Peer Evaluation Metrics are not Reliable Measures of Quality or Impact
Bibliographic record
Abstract
A Review of: Ortega, J. L. (2019). Exploratory analysis of Publons metrics and their relationship with bibliometric and altmetric impact. Aslib Journal of Information Management, 71(1), 124– 136. https://doi.org/10.1108/AJIM-06-2018-0153 Abstract Objective – To analyze the relationship between scholars’ qualitative opinion of publications using Publons metrics and bibliometric and altmetric impact measures. Design – Comparative, quantitative data set analysis. Setting – Maximally exhaustive set of research articles retrievable from Publons. Subjects – 45,819 articles retrieved from Publons in January 2018. Methods – Author extracted article data from Publons and joined them (using the DOI) with data from three altmetric providers: Altmetric.com, PlumX, and Crossref Event Data. When providers gave discrepant results for the same metric, the maximum value was used. Publons data are described, and correlations are calculated between Publons metrics and altmetric and bibliometric indicators. Main Results – In terms of coverage, Publons is biased in favour of life sciences and subject areas associated with health and medical sciences. Open access publishers are also over-represented. Articles reviewed in Publons overwhelmingly have one or two pre-publication reviews and only one post-publication review. Furthermore, the metrics of significance and quality (rated on a 1 to 10 scale) are almost identically distributed, suggesting that users may not distinguish between them. Pearson correlations between Publons metrics and bibliometric and altmetric indicators are very weak and not significant. Conclusion – The biases in Publons coverage with respect to discipline and publisher support earlier research and suggest that the willingness to publish one’s reviews differs according to research area. Publons metrics are problematic as research quality indicators. Most publications have only a single post-publication review, and the absence of any significant disparity between the scores of significance and quality suggest the constructs are being conflated when in fact they should be measuring different things. The correlation analysis indicates that peer evaluation in Publons is not a measure of a work’s quality and 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 | 0.208 | 0.592 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.037 | 0.051 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".