Bibliographic record
Abstract
Canada's public higher education system is in trouble.The economic and social benefits of the Canadian university system are widely seen as a public good, which raises a pressing question: Why should we aspire to anything less than a great system?For that to happen, everything about the way universities currently operate, from the boardroom to the classroom, must change -but this kind of operational and public policy transformation will not be easy.Nothing Less than Great provides an expert analysis of the current state and challenges of Canada's university system, looking for positive change by reclaiming what a university is meant to offer for society and for citizens.Harvey P. Weingarten begins with the fundamental question that all students must ask about higher education: Is it worth going to university?From there, he stresses the need for transparency about what universities do and what they accomplish, addresses the importance of modernizing curriculum to emphasize skills over content, and provides recommendations for reform.Exploring how universities might -and should -change to reclaim their central purpose for Canadians, Nothing Less than Great will be of interest to anyone who cares about the future of our country and the important role universities play in determining that future.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.858 | 0.698 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".