Comments welcomed. INSURANCE FOR THE UNEMPLOYED: CANADIAN REFORMS AND THEIR RELEVANCE FOR THE UNITED STATES*
Bibliographic record
Abstract
July of 1996, the main reforms we helped conceive, and which were described and discussed in our original paper, were enacted in Bill C-12, the “EI Act”. Key provisions of the EI Act and the reform process that resulted in the passage of that Act are examined in this revised paper. Additional financial support for this research was received from the Social Sciences and Humanities Research Council of Canada. Alice Nakamura is deeply indebted to Ging Wong and other researchers at the Department of Human Resources Development Canada (HRDC) and Statistics Canada as well as to Lloyd Axworthy and the other members of the Axworthy Social Security Reform Task Force for ideas shared and critiqued and for making this experience possible. The authors also thank Bill Alpert, John Cragg, David Green, Jonathan Kesselman, Lars Osberg, Emi Nakamura, Shelley Phipps, Kathleen Sayers, Wayne Vroman, Stephen Woodbury and the participants in a seminar at the U.S. Department of Labor for helpful discussions on social policy and comments on earlier versions of this paper, with the usual disclaimer that all opinions and shortcomings of the paper are our sole responsibility. Early in 1994, Lloyd Axworthy, then Minister of Human Resources Development, launched
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.086 | 0.018 |
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".