Joining the meta-research movement: A bibliometric case study of the journal <em>Perspectives on Medical Education</em>
Bibliographic record
Abstract
PURPOSE: To conduct a bibliometric case study of the journal Perspectives on Medical Education (PME) to provide insights into the journal's inner workings and to "take stock" of where PME is today, where it has been, and where it might go. METHODS: Data, including bibliographic metadata, reviewer and author details, and downloads, were collected for manuscripts submitted to and published in PME from the journal's Editorial Manager and Web of Science. Gender of authors and reviewers was predicted using Genderize.io. To visualize and analyze collaboration patterns, citation relationships and term co-occurrence social network analyses (SNA) were conducted. VOSviewer was used to visualize the social network maps. RESULTS: Between 2012-2019 PME received, on average, 260 manuscripts annually (range = 73-402). Submissions were received from authors in 81 countries with the majority in the United States (US), United Kingdom, and the Netherlands. PME published 518 manuscripts with authors based in 31 countries, the majority being in the Netherlands, US, and Canada. PME articles were downloaded 717,613 times (mean per document: 1388). In total 1201 (55% women) unique peer reviewers were invited and 649 (57% women) completed reviews; 1227 (49% women) unique authors published in PME. SNA revealed that PME authors were quite collaborative, with most authoring articles with others and only a minority (n = 57) acting as single authors. DISCUSSION: This case study provides a glimpse into PME and offers evidence for PME's next steps. In the future, PME is committed to growing the journal thoughtfully; diversifying and educating editorial teams, authors, and reviewers, and liberating and sharing journal data.
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How this classification was reachedexpand
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 | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.125 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.045 | 0.084 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".