Bibliographic record
Abstract
Thomas Courchene is the author or editor of numerous books and has published hundreds of academic papers on a wide range of Canadian public policy issues . Thinking Outside the Box honours his outstanding contributions to Canadian public policy thinking and practice. Research papers included in the volume span several of the key areas in which Courchene has made significant contributions, including federalism, macro-economic policy, the unique constitutional challenges that pose difficulties for policy in Canada, and economic well-being. An introduction by Gilles Paquet reflects on Courchene’s unique contribution to Canadian public policy. Courchene also provides an introductory essay, reflecting on his policy thinking over the course of his career. Contributors include: on federalism and economic policy, Robin Boadway, Serge Coulombe, Jean-François Tremblay, Katherine Fierlbeck, Bryne Purchase, Michael J. Prince, Donald J. Savoie, and Lisa M. Powell; on economic policy, Pierre Fortin, Peter Howitt, Alex Ripley and Stephen Clarkson; on Canada’s constitutional challenges, David Cameron and Kathy L. Brock; and on economic inequality, Miles Corak, Brian Murphy and Michael Veall, and Michael G. Abbott and Charles M. Beach.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.019 |
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".