“I have no idea how I will get a stipend”: the impact of COVID-19 on graduate students’ financial security
Bibliographic record
Abstract
In this study, we investigated graduate students’ financial security during the COVID-19 pandemic. In the Spring 2022 semester, the continued changes brought on by the precautionary measures implemented presented difficulties for both faculty and graduates. In this mixed methods study, we surveyed graduate students (N = 258) at a public research university to explore the pandemic’s effects on their financial security. Through the sign test analyses, we found a significant decrease in graduate students’ financial security scores during the pandemic, and particular groups of students were more susceptible to decreased financial security. The thematic analysis further explored the financial security decrease and identified inflation and economic downturns, and the suspension of work or financial support as two main challenges graduate students confronted during the pandemic. Both quantitative and qualitative findings reflected the financial stress and hardship confronted by graduate students during the pandemic. It was implied that extra financial support and consultation should be provided to students with meager stipends or student loans. The results further informed the academic community to foster the culture of care inside academia, by addressing graduate students’ financial concerns properly in the post-pandemic period.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".