Student publishing in peer reviewed journals: Evidence from the <i>International Political Science Review</i>
Bibliographic record
Abstract
Abstract Publishing in peer‐reviewed journals has become an essential requirement for PhD students wishing to pursue a career in academia. Yet, there are few studies of student publishing and little discussion of norms around attribution of authorship for student research collaborators. (1) How often do students feature as submitters and authors in political science journals? (2) In what format (i.e., solo author, co‐author, multiple authors) do students normally submit and publish? (3) Are there gender differences in student submission and publication rates between male and female students? This article uses 2 years of data from the International Political Science Review (IPSR; i.e., 2019 and 2020) to answer these questions. Mainly using cross‐tabulations, we found that just one in eight submitting authors was a student (i.e., undergraduate and postgraduate). In terms of acceptance rates, students had generally lower acceptance rates than faculty. Yet, there were also important differences within the student body. As expected PhD students were more successful than undergraduate and masters' students, and in line with general disciplinary publishing patterns, female PhD students had a higher publication success rate than their male colleagues.
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 | 0.071 | 0.374 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".