Four Studies on the Canadian Equalization System
Bibliographic record
Abstract
On January 28 and 29, 2014, the University of Calgary’s School of Public Policy (SPP) sponsored a conference on equalization reform. The event provided a forum for current and former federal and provincial officials, academics from Canadian, Spanish and German universities, as well as students from the Master of Public Policy program at the SPP, to explore the issues surrounding this often-contentious program. Four leading Canadian academics in the field, Bev Dahlby, Jim Feehan, Ergete Ferede and Marcelin Joanis, delivered papers at the conference, each advocating a particular path forward for Canada’s equalization system. This commentary provides a summary of their proposals and arguments for reform.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.058 | 0.020 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".