Stretching the Federation: The Art of the State in Canada
Bibliographic record
Abstract
Stretching the Federation deals with such topics as world trends in federations, whether decentralization is inherently conservative, decentralization in health care, possible reallocations of programs and tax revenues, overlap and duplication in service delivery, and fiscal decentralization. Stretching the Federation is a timely and topical collection of cutting-edge thinking about the future of the Canadian federation. Contributors include Paul Boothe (Alberta), David Cameron (Toronto), Thomas Courchene (Queen's), Derek Hermanutz (Alberta), Michael Keating (University of Western Ontario), Harvey Lazar (Queen's), Evert Lindquist (Toronto), Antonia Maloni (McGill), Peter Meekison (Victoria), Alain Noel (Universite de Montreal and Berkeley), Bill Robson (C.D. Howe Institute), and France St. Hilaire (IRPP).
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.040 | 0.026 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".