Replication Data for: The American Political Science Review during the COVID-19 Pandemic
Bibliographic record
Abstract
On June 1, 2020, a little more than two months after the World Health Organization's pandemic declaration, we assumed leadership of the American Political Science Review (APSR), making it difficult to isolate the pandemic's effect on new submissions and review processes. In this research note, we describe submission and review patterns in the two and half years before and after the pandemic's beginning and editorial transition. We offer some tentative conclusions. The timing of the editorial transition and our public commitments to broaden the reach of the journal may help explain why new submissions to the APSR increased during the the pandemic. At the APSR, our commitment to substantive diversity may have also contributed to greater representational diversity among submitting authors. In our experience, reviewers were less likely to complete reviews during the first years of the pandemic, but by inviting more reviewers per manuscript, our team was able to improve review times overall. This strategy may not work as well for smaller journals that already struggle to secure reviews.
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 | 0.110 | 0.612 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.029 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.190 | 0.057 |
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".