Canada and the United States : differences that count
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it