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Record W4283656970 · doi:10.1177/00084298221102926

And then there were none: Regional dynamics of non-religious identities, beliefs and practices among Canadian millennials

2022· article· en· W4283656970 on OpenAlexafffundvenueabout
Sarah Wilkins‐Laflamme

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReligious diversitySpiritualityDemographicsSociology of religionDiversity (politics)SociologyReligious studiesEthnologyHumanitiesGender studiesDemographyAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

Religious nones - or, in other words, those who say that they have no religion when asked - are one of the fastest growing demographics in Canada, especially among young adults aged 18-35. Using statistical data from a 2019 Millennial Trends Survey, a diversity of approaches to religion, spirituality and non-religion can be seen within this broad category of individuals. Based on these findings, the author argues that the two main theoretical frameworks of 'secular transition' and 'spiritual but not religious' should be understood as complementary, rather than contradictory, in understanding the religious none phenomenon. Evidence of five distinct regional patterns of religious nones across the country is found, which are designated as 'spiritual British Columbia', 'dispersed Prairies', 'vestigial and uncertain Ontario', 'non-believing Quebec' and 'stigmatized Atlantic Canada' nones.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.399
Teacher spread0.343 · 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 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

Citations17
Published2022
Admission routes4
Has abstractyes

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