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
BACKGROUND: The demand for peer reviewers is often perceived as disproportionate to the supply and availability of reviewers. Considering characteristics associated with peer review behaviour can allow for the development of solutions to manage the growing demand for peer reviewers. The objective of this research was to compare characteristics among two groups of reviewers registered in Publons. METHODS: A descriptive cross-sectional study design was used to compare characteristics between (1) individuals completing at least 100 peer reviews ('mega peer reviewers') from January 2018 to December 2018 as and (2) a control group of peer reviewers completing between 1 and 18 peer reviews over the same time period. Data was provided by Publons, which offers a repository of peer reviewer activities in addition to tracking peer reviewer publications and research metrics. Mann Whitney tests and chi-square tests were conducted comparing characteristics (e.g., number of publications, number of citations, word count of peer review) of mega peer reviewers to the control group of reviewers. RESULTS: A total of 1596 peer reviewers had data provided by Publons. A total of 396 M peer reviewers and a random sample of 1200 control group reviewers were included. A greater proportion of mega peer reviews were male (92%) as compared to the control reviewers (70% male). Mega peer reviewers demonstrated a significantly greater average number of total publications, citations, receipt of Publons awards, and a higher average h index as compared to the control group of reviewers (all p < .001). We found no statistically significant differences in the number of words between the groups (p > .428). CONCLUSIONS: Mega peer reviewers registered in the Publons database also had a higher number of publications and citations as compared to a control group of reviewers. Additional research that considers motivations associated with peer review behaviour should be conducted to help inform peer reviewing activity.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.274 | 0.295 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.099 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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