Replication Data for: The American Political Science Review during the COVID-19 Pandemic
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
On June 1, 2020, a little more than two months after the World Health Organization's pandemic declaration, we assumed leadership of the <i>American Political Science Review</i> (<i>APSR</i>), 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 <i>APSR</i> increased during the the pandemic. At the <i>APSR</i>, our commitment to <i>substantive</i> diversity may have also contributed to greater <i>representational</i> 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.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.012 |
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