Bibliographic record
Abstract
Why do the United States and Canada have such divergent political cultures when they share one of the closest economic and cultural relationships in the world? Canadians and Americans consistently disagree over issues such as the separation of church and state, the responsibility of government for the welfare of everyone, the relationship between federal and subnational government, and the right to marry a same-sex partner or to own an assault rifle. In this wide-ranging work, Jason Kaufman examines the North American political landscape to draw out the essential historical factors that underlie the countries' differences. He discusses the earliest European colonies in North America and the Canadian reluctance to join the American Revolution. He compares land grants and colonial governance; territorial expansion and relations with native peoples; immigration and voting rights. But the key lies in the evolution and enforcement of jurisdictional law, which illuminates the way social relations and state power developed in the two countries. Written in an accessible and engaging style, this book will appeal to readers of sociology, politics, law, and history as well as to anyone interested in the relationship between the United States and Canada
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".