Education Debt and Making a Career Choice in the Public, Private, and Nonprofit Sectors
Bibliographic record
Abstract
We surveyed a sample of Millennial college seniors who are job seekers to investigate if: (1) education debt discourages students from pursuing (lower paying) public or nonprofit careers, and (2) whether PSM overrides the considerations students might make about entering lower paying sectors (i.e., public and nonprofit sectors) as their education debt rises. To our surprise, we find that education debt is related to a greater propensity to select lower paying public sector careers but not lower paying nonprofit jobs (except for those with high debt loads). Moderate levels of PSM are required for students to select public sector careers and high levels of PSM are required for students to select nonprofit careers with rising education debt. We conclude that individuals with a high debt load may be attracted to public policy setting and select public sector careers, while those who display empathy and compassion are attracted to nonprofit work in service to others.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".