National Identities and Images of the Other in a Canadian–American Borderlands Region: Value Difference or Borderlands Convergence?
Bibliographic record
Abstract
This article explores differences in national identities and orientations toward “the other” among Canadian and American students who attend geographically proximate universities in the southern Ontario/Upstate New York borderlands region. Drawing on descriptions of Canada–US cultural differences regarding national identities and views of “the other” from the work of Seymour Martin Lipset (and his critics) among Canadians and Americans at large, the authors uncover some evidence that is generally (although not universally) supportive of his characterizations. We then narrow our focus to compare the orientations of respondents who were raised in the bi-national borderlands region. Although the magnitude of difference between the views of the full sample and those of this geographically restricted group are generally not large, a multivariate test comparing scores on an additive index of cross-border affinity does show up robust evidence of increased affinity sentiments among those raised in these geographically proximate areas. Interestingly, however, the authors did not find that these heightened cross-border affinities are related to the frequency with which the individual crosses the border or to the existence of cross-border kinship networks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".