Bibliographic record
Abstract
This thoroughly revised edition of Canada and the United States: Differences that Count continues to address, in a timely way, key institutions and policy areas, adding new chapters on welfare, race and public policy, values, demography, crime, the environment, conflict resolution, and federalism. Data sources for further research have also been included. As in the previous editions, the book does not assume that differences are increasing or decreasing or that one country is than the other. In a straightforward and readable manner, the book looks at the Canadian way and the American way of doing things. From health care to crime (and punishment); from immigration to race and public policy; from tax regulations to the environment; from values to prime ministers and presidents there are as many differences as there are similarities in the way the two countries do things, and not infrequently it turns out that the similarities and differences are not as we have assumed them to be. In Canada and the United States: Differences that Count, Third Edition, leading authorities compare and contrast the Canadian and the American experiences. They do so in the hope of creating a better understanding of the similarities and differences so that policy-makers, students, and ordinary citizens in each of the two countries may learn from the experiences of the other.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".