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Record W4384911788 · doi:10.3138/ijcs-2022-0006

National Identities and Images of the Other in a Canadian–American Borderlands Region: Value Difference or Borderlands Convergence?

2023· article· en· W4384911788 on OpenAlexaffvenueabout
Munroe Eagles, Nick Baxter‐Moore

Bibliographic record

VenueInternational Journal of Canadian Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsBrock University
Fundersnot available
KeywordsKinshipConvergence (economics)Multivariate statisticsAffinitiesGenealogyGender studiesGeographySociologyValue (mathematics)DemographyEconomic geographyEthnologyAnthropologyHistoryEconomic growthStatistics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.352
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes3
Has abstractyes

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