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Record W4252137347 · doi:10.1386/ctl.10.3.311_1

Incorporating the study of religion into Canadian citizenship education: More than the political

2015· article· en· W4252137347 on OpenAlexaboutno aff
Margie Patrick

Bibliographic record

VenueCitizenship Teaching and Learning · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipReligious identityFunctional illiteracyReligious pluralismReligious educationPoliticsSociologyPluralism (philosophy)Gender studiesPopulationSocial scienceSalience (neuroscience)SecularismPolitical scienceLawEpistemologyPedagogyPsychology

Abstract

fetched live from OpenAlex

Abstract To date Canadian citizenship education in English Canada has largely ignored religion. Given the religious diversity of the Canadian population and the increasing political salience of religion in national and international events, the marginalization of religion within citizenship education is no longer tenable. Citizenship education is integrally connected with diversity policies, and the religious illiteracy common among Canadians harms those who belong to minority religions, many of whom are first- and second-generation immigrants. More specifically, religious illiteracy breeds misperceptions about religious adherents who highly identify with their religious identity and it hinders the ability of society to take religious differences seriously. Despite the links between religious and citizenship education however, there are concerns about reducing religion to its political expediency of addressing religious diversity and pluralism. In this article I draw on research about religion conducted in various disciplines that promote citizenship education and address religious illiteracy without reducing religion to its political functions. The three areas studied are religious pluralism, religious identity and inter-religious dialogue.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.344
Teacher spread0.305 · 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.

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

Citations3
Published2015
Admission routes1
Has abstractyes

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