The Realities Facing Graduate Students: Before, During, and After the 2020 COVID-19 Pandemic — CORRIGENDUM
Bibliographic record
Abstract
The authors clarify that they conducted an analysis of APSA digital surveys launched in 2018, 2020, and 2022.The APSA surveys asked members and non-members in the field of political science about a range of issues including employment, student academic climate, well-being, and other issues related to the status of participation in the industry.• Survey authorship and distribution: The authors clarify that the surveys were distributed by APSA and were not designed or distributed by the APSA Committee on the Status of Graduate Students.• Accurate N: The N for each survey is accurately reflected as follows: From the data collected by APSA in 2018, our analysis uses a sample of (N=245).From the data collected in 2020-21, we use a sample of (N=317).And from the data collected in 2022, we
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.030 |
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".