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Record W4403293784 · doi:10.1093/publius/pjae008

Subjective Perceptions of Difference in Multi-level States: Regional Values, Embeddedness, and Bias in Canadian Provinces

2024· article· en· W4403293784 on OpenAlexafffundabout
Ailsa Henderson, Antoine Bilodeau, Luc Turgeon, Stephen White

Bibliographic record

VenuePublius The Journal of Federalism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsCarleton UniversityUniversity of OttawaConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEmbeddednessPerceptionPolitical scienceDemographic economicsRegional sciencePsychologyEconomic geographyGeographySociologySocial scienceEconomics

Abstract

fetched live from OpenAlex

Abstract Subnational variations in political culture and policy attitudes are a hallmark feature of multilevel systems of government, yet we know comparatively little about how and why citizens of these systems subjectively perceive regional differences in political values. Using data from a specially commissioned survey under the auspices of the Provincial Diversity Project, this article analyses subjective perceptions of difference across provinces in Canada. It shows that individuals believe their regions have distinct political values, but also that they systematically overestimate that difference. In their estimations of regional distinctiveness, individuals are informed by the value profiles of the regions in which they live, but also by their own policy preferences. The findings clarify the salience of internal boundaries within multilevel or federal states, and enable us to understand how myths of distinctiveness flourish, but also provide an important extension on debates about misperceptions in politics.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.541
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.084
GPT teacher head0.338
Teacher spread0.254 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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