Bibliographic record
Abstract
The prospect of tuition fee increases for public sector universities has attracted an enormous amount of attention in recent years as governments in all industrialized countries have responded to the converging pressures of increased demands for higher education and rising costs of competing areas of social spending. I show that this dilemma is fast approaching a critical point in both Canada and the UK. As contemporary society become "knowledge societies," postsecondary systems become "complex," requiring a sensitive political blending of different institutional goals, such as accessibility, diversity of mission, critical thought, relevance, and social usefulness. This article draws upon the policy model of income contingent repayment (ICR) as a touchstone for debates and larger proposals about addressing the future of higher education reform. My hope is to show the partial shortcomings of the traditional alternatives: reliance on state-provided subsidy on the one hand and deregulated and flexible fees on the other. I then argue that changes in the social and political meaning of participation in higher education might warrant taking a second look at the "smart funding" approach represented by ICR proposals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".