Defining Moments and Recurring Myths: Comparing Canadians and Americans after the American Revolution*
Bibliographic record
Abstract
Dans cet article, nous examinons la célèbre thèse de S.M. Lipset, qui affirme que la révolution américaine a créé des differénces durables entre les valeurs canadiennes et américaines. Nous reconsidérons tout d'abord l'affirmation centrale de la thèse selon laquelle l'exode des loyalistes vers le Canada a ancré de façon permanente des systémes de valeurs distincts dans les deux sociétés. Notre analyse suggère que, au sein de la population générate, les loyalistes n'ont joué qu'un rôle négligeable dans la promotion de différences fondamentales. Nous comparons ensuite les deux sociétés sur le plan historique en utilisant plusieurs indicateurs ‐ classes sociales et structure économique, compositions ethnique et religieuse, modèles d'immigration et d'urbanisation, culture et organisation politiqUes ‐ afin de cerner des divergences importantes dans l'expérience et le milieu dans lequel vivaient les deux peuples, divergences qui auraient pu favoriser ces différences. Nous concluons que les deux populations se ressemblaient étonnamment, ce qui sous‐entend qu'elles partageaient probablement le même quotidien, les mêmes perspectives et valeurs au cours de la période révolutionnaire et pendant plusieurs dizaines d'années par la suite. This paper examines S.M. Lipset's widely known thesis that the American Revolution engendered lasting differences in Canadian and American values. We first reconsider the key claim of the thesis: that the Loyalist exodus to Canada permanently entrenched distinct value systems in the two societies. Our analysis indicates that, within the general population, the Loyalists had a negligible effect in promoting core differences. We then compare the two societies historically on several indicators—class and economic structure, ethnic and religious composition, immigration and urbanization patterns, and political culture and organization—to identify significant divergences in the peoples' backgrounds and experiences that may have promoted major differences. We find that the two populations were strikingly alike, suggesting that their everyday lives, outlooks, and values were probably quite similar during the Revolutionary era and for several decades afterward.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".