Government Student Loan Default: Differences between Graduates of the Liberal Arts and Applied Fields in Canadian Colleges and Universities
Bibliographic record
Abstract
Les programmes de prêts étudiants du gouvernement sont de plus en plus disponibles pour permettre aux étudiants de familles défavorisées un meilleur accès à l'éducation. En revanche, il est inquiétant d'observer des niveaux d'endettement élevés chez les étudiants et les difficultés qu'ont certains à rembourser ces dettes. Dans cette étude, des données de l'Enquête Nationale auprès des Diplômes 2005 (END) de Statistiques Canada sont analysées dans le but d'établir des liens entre les domaines d'étude et le non‐remboursement des dettes sur les prêts étudiants du gouvernement pour un échantillon de diplômés des collèges et universités. Les analyses prennent en compte les effets de facteurs alternatifs liés au statu socio‐économique et aux revenus. Les résultats démontrent que le niveau d'éducation (collégial versus universitaire) et les domaines d'étude sont des déterminants importants du risque de non‐remboursement des dettes sur les prêts étudiants deux ans après l'obtention du diplôme. De plus, ces résultats sont stables en tenant compte des différences de revenus. Government student loan programs have become increasingly available to provide opportunities and upward mobility for students of disadvantaged backgrounds. Rising student debt and its impact on the repayment experiences of recent postsecondary graduates has become an important concern. This study employs data from Statistics Canada's 2005 National Graduates Survey to examine the relationship between field of study and loan default on government‐supported student loans for graduates of community college and baccalaureate‐level university programs when controlling for many factors relating to sociodemographic characteristics and earnings. Overall, both level of schooling (college versus university) and field of study are significant predictors of whether graduates report defaulting on their government student loans within two years of graduation. However, these findings are relatively unrelated to earnings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".