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Record W4391881043 · doi:10.1177/17461979241227845

From religious citizen to multicultural citizen: Changing conceptualizations of citizenship and belonging in Canada

2024· article· en· W4391881043 on OpenAlexafffundabout
Lori G. Beaman

Bibliographic record

VenueEducation Citizenship and Social Justice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of Ottawa
FundersAustralian Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsCitizenshipMulticulturalismCitizen scienceGood citizenshipPolitical scienceSociologyGender studiesLawPolitics

Abstract

fetched live from OpenAlex

The religious landscape in Canada has shifted dramatically during the past 50 years, from a country whose population and social institutions were inextricably entangled with Christianity to one which has an increasing number of people who do not identify with a religion at all. Citizenship in this context has shifted from a nation whose imaginary was predominantly Christian, with diversity conceptualized in rather limited ways to one characterized by (non)religious diversity and a multicultural reality. Yet, there are growing pains as majoritarian religion confronts a changing power dynamic and the new diversity. These growing pains have potentially negative implications for education. This article considers the new diversity, the articulation of religious symbols and practices as culture, and the implications of these for citizenship and living well together.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0420.041
Scholarly communication0.0140.006
Open science0.0030.011
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.304
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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