Introduction: Pandemic and Post-Pandemic Publication Patterns in Political Science
Bibliographic record
Abstract
The COVID-19 pandemic triggered rapid transformations across the globe. Probably no other event in the past 50 years has changed the working environment more comprehensively. When the pandemic started in February and March of 2020, university campuses all over the globe shut down in less than a week. Most remained closed or had heavily restricted access for almost two years, depending on the country and the city. Overnight online teaching replaced in-person instruction; all professional and student interactions moved to Zoom, Teams, or Skype; academic conferences either did not take place or moved to an online format; and field research became almost impossible. In addition, contact restrictions, lockdowns, curfews, and homeschooling were unprecedented challenges for many people, especially members of the academic community who had small children (Del Boca et al. 2020). It is important to note that even in normal times women bear the greatest burden of childcare, social care for older people, and general household tasks. The COVID-19 pandemic quickly amplified these disparities (Ohlbrecht and Jellen 2021; Yerkes et al. 2022). Discussions emerged in many sectors, including academia, about specific ways that the pandemic was impacting professional lives, especially those of women. Given the acute pressure to publish in most higher-education institutions, it is important to evaluate the effect that the pandemic had on this central aspect of scholarly careers.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".