Student Repayment Crisis and the Value of Higher Education and the Economy in California’s Kern County
Bibliographic record
Abstract
The cost of post-secondary education (PE) continues to increase, which has contributed to elevating federal loan demand, and as of the fourth quarter of 2020, equaling a debt of $1.56 trillion in the US. The purpose of this research was to compare two post-secondary institutions for specific alignment with the local labor market, examine institutional economic benefits and costs, and impact of loan default. Bakersfield College (BC) and California State University, Bakersfield (CSUB) are both public, Hispanic Serving Institutions, in central California. Despite similarities, loan default rates of each institution differ; six-year mean rates, 24.6% at BC, 7.7% at CSUB. The analysis revealed that although the top degrees at BC and CSUB did not align well with local labor market demands, the individual and institutional economic benefit exceeds the costs. Importantly, both the individual and institutional economic benefits are highly dependent on completing the degree, the time to graduation, and then entering the labor market. The value of this research, specifically a cost-benefit analysis to examine recent trends in local wages, tuition fees, defaults rates, poverty, and alignment with the local labor market, provides insight on the impact of local PE on the individual and the community, providing both educational and economic policy direction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".