Bibliographic record
Abstract
Since the inception and design of Canada's Employment Insurance (EI) program, the Canadian economy and labour market have undergone dramatic changes. It is clear that EI has not kept pace with those changes, and experts and advocates agree that the program is no longer effective or equitable. Making EI Work is the result of a panel of distinguished scholars gathered by the Mowat Centre Employment Insurance Task Force to analyze the strengths, weaknesses, and future directions of EI. The authors identify the strengths and weaknesses of the system, and consider how it could be improved to better and more fairly support those in need. They make suggestions for facilitating a more efficient Canadian labour market, and meeting the human capital requirements of a dynamic economy for the present and the foreseeable future. The chapters that comprise Making EI Work informed the task force's final recommendations, and form an engaging dialogue that makes the case for, and defines the parameters of, a reformed support system for Canada's unemployed. Contributors include Ken Battle (Caledon Institute of Social Policy), Robin Boadway (Queen's University), Allison Bramwell (University of Toronto), Sujit Choudhry (New York University School of Law), Kathleen M. Day (University of Ottawa), Ross Finnie (University of Ottawa), Jean-Denis Garon (Queen's University), David Gray (University of Ottawa), Morley Gunderson (University of Toronto), Ian Irvine (Concordia University), Stephen Jones (McMaster University), Thomas R. Klassen (York University), Michael Mendelson (Caledon Institute of Social Policy), Alain Noël (Université de Montréal), Michael Pal (University of Toronto Faculty of Law), W. Craig Riddell (University of British Columbia), William Scarth (McMaster University), Luc Turgeon (University of Ottawa), Leah F. Vosko (York University), Stanley L. Winer (Carleton University), Donna E. Wood (University of Victoria), and Yan Zhang (Statistics Canada).
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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 | 0.051 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.040 | 0.027 |
| Scholarly communication | 0.045 | 0.026 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.035 | 0.020 |
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".