Bibliographic record
Abstract
Abstract This chapter examines Government of Canada policies that profoundly altered Canada’s research universities. It argues that the federal university research policies are examples of successful public policies. The chapter shows how such initiatives as the Canada Research Chairs, the Canada Foundation for Innovation, and a program for the federal funding of the indirect costs of federal research grants were priorities for Liberal prime minister Jean Chrétien’s majority governments in the late 1990s and early 2000s. It notes how these programs were made possible by the governments’ receipt of unexpected budget surpluses. Chrétien insisted that a priority for the expenditure of these surpluses was university research, which he saw as essential to building Canada as a force in the emerging ‘knowledge economy’. A small group of university presidents, advisors in the Prime Minister’s Office, and senior officials in the Department of Finance spearheaded the policies. The policies established Ottawa as the dominant force in Canadian university research policy and strengthened federal links with major research universities including the University of Toronto, University of British Columbia, and McGill University. The chapter also shows how Chrétien’s policies have stood the test of time and were supported by Prime Minister Harper’s Conservative governments. The chapter notes how the federal policies created a clear set of policy winners and losers.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".