The Age of Satisficing? Juggling Work, Education, and Competing Priorities during the COVID-19 Pandemic
Bibliographic record
Abstract
The coronavirus disease 2019 pandemic continues to shape individuals' decisions about employment and postsecondary education. The authors leverage data from a longitudinal qualitative study of educational trajectories to examine how individuals responded to the shifting landscape of work and education. In the final wave of interviews with 56 individuals who started their postsecondary education at a community college 6 years ago, the authors found that most respondents described engaging in satisficing behaviors, making trade-offs to maintain their prepandemic trajectories where possible. More than a quarter of individuals, primarily those with access to fewer resources, described trajectories fraught with insecurity; they struggled to juggle competing obligations, especially in the face of an unpredictable labor market. A small portion of participants described making optimizing decisions, which were sometimes risky, to prioritize their aspirations. These descriptive patterns may partially explain mechanisms shaping recent shifts in employment and postsecondary education, including lower labor-market engagement and declines in college enrollment.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".