The Making of a Policy Regime: Canada's Student Finance System since 1994
Bibliographic record
Abstract
This paper examines the pattern of decision-making, lobbying, and influence that led to the landmark series of federal student assistance policies introduced by Jean Chrétien’s Liberal government in the late 1990s. The package of new initiatives—dubbed the Canada Opportunities Strategy—not only partially reversed an earlier period of fiscal restraint but also brought a new emphasis on direct forms of student assistance such as grants, bursaries, and tax credits. However, programs such as the Canada Millennium Bursaries and the Canada Education Savings Grants, despite their focused approach and innovative structure, came to be regarded as weak policy tools when measured against their ostensible goals of widening access to post-secondary education and efficiently targeting student assistance on the basis of need. The new policy regime also failed to fulfil nearly two decades of previous efforts by policy-makers to transform Canada’s student debt program into a systematic income-contingent loan program. We offer explanations of this pattern of policy inconsistency and incoherence by examining the awkward challenges of intergovernmental relations in the Canadian federal system as well as the fragmentation and competing goals now evident in student assistance policy networks. We contrast the student finance policy regime with the arguably more coherent set of research and innovation policies established by the federal government during the same period. We use policy network analysis as our theoretical framework, and we use data from our extensive interviews with higher education stakeholders and policy-makers to provide empirical support.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".