Poor Students, Well-Paid Lawyers: Post-Graduation Income-Contingent Tuition Fees for Law Schools
Bibliographic record
Abstract
Law Schools in Canada have traditionally been funded mainly from two sources: government grants and student-paid tuition fees. For many years, law schools have faced a funding crisis because of inadequate government grants. Their response has been to increase up-front tuition fees. Such fees affect the accessibility of legal education for lower-income applicants, and impose other severe burdens on students. This article proposes a new model: post-graduation tuition fees contingent upon the income earned by the graduate. This novel approach would not look to government to increase its subsidies, but would look to students to contribute a larger share. Income-contingent tuition fees are similar to income-contingent loans in that the extent of obligation varies with level of income. But they are different in that the financial partner is the university, not the government, and the student does not owe debt or pay interest. For students, this is a risk-free approach that reduces the economic disincentives to attend law school. For law schools, it would provide a source of income that is more secure, predictable, and generous than current sources. Law schools would be investing in the future professional success of their graduates, and students would be more likely to make career choices that were not motivated by the need to repay massive debt after graduation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".