Bibliographic record
Abstract
Canadians and Americans share a common border and, to a large extent, a common culture. For the most part we watch the same movies and television shows, listen to the same music, and follow the same sports. However, there are some notable issues that divide us. We Canadians are the ones with a universal single-payer health care system, an unelected senate, and gun control. And we have laws against hate speech. How is this latter disparity to be explained? One possibility, which has been advanced by several commentators, is that it reflects broader and deeper differences between the two countries’ social/political cultures. In his documentary Bowling for Columbine Michael Moore hypothesized that Canadians tend to be less individualistic than Americans, more inclined to favour the collective good even at the cost of some individual freedom. My Toronto colleague Joseph Heath has defended a more scholarly version of this hypothesis in his book The Efficient Society. So perhaps the greater tolerance of hate speech in the United States can be understood only against this backdrop of American individualism and libertarianism, on the one hand, and Canadian collectivism and egalitarianism, on 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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".