Paying back student loans: Demographic, human capital and other correlates of default and repayment difficulty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Government‐sponsored student loans have emerged over the decades as a primary method of financing post‐secondary education across most North American jurisdictions. Despite this, the empirical literature examining the correlates of repayment difficulty and default in Canada has remained stagnant in recent years. This study taps into an underutilised data source—the 2013 National Graduates Survey—to examine the relationship between demographics, human capital, borrowing behaviour and other known predictors and repayment difficulty. Our logistic regression models demonstrate that disability status, geographic region and borrowing behaviour are correlated with loan default and repayment difficulty, while failing to verify the existence of other demographic effects routinely found in the existing literature. We discuss the implications of these findings, along with multiple avenues for further empirical work on this topic within Canada.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it