Policy Forum: Five Reasons To Be Skeptical About the Repayment of Canada's Student Loans Through the Tax System
Bibliographic record
Abstract
Since the world's first tax-system-based income-contingent repayment system for the repayment of student loans was introduced in Australia in 1989, there have been suggestions that Canada should adopt a similar system. But there has been little discussion of the practicalities involved in introducing a new system where there is joint federal and provincial involvement and where the new system would replace a pre-existing and generous, if incomplete, form of income-contingent repayment (ICR). Joint federal and provincial involvement is a problem unique to Canada, and replacement of the existing system becomes problematic when that system is more generous than the proposed alternative. In this article, we identify five key stumbling blocks that make us skeptical about the prospects of switching to tax-system-based repayment of student loans in Canada: the need for intergovernmental cooperation; additional responsibilities for the tax authorities; potential costs to employers from further complicating the withholding system; challenges if the new system were to try to fit the current program parameters into the tax system efficiently; and the political challenge of gaining student support. While there are certainly benefits to administering ICR through the tax system, these need to be weighed against the costs.
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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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.053 | 0.021 |
| Scholarly communication | 0.027 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.042 | 0.042 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".