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Record W7162418452 · doi:10.1558/jbasr.32512

Ticking “no religion”

2014· article· W7162418452 on OpenAlexaboutno aff
Simeon Wallis

Bibliographic record

VenueJournal of the British Association for the Study of Religion (JBASR) · 2014
Typearticle
Language
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsnot available
Fundersnot available
KeywordsSecularityValue (mathematics)Quarter (Canadian coin)Relevance (law)Qualitative researchCensusField (mathematics)Humanism

Abstract

fetched live from OpenAlex

In the 2011 Census in England and Wales, 14.1 million people, about a quarter of the population, reported that they have “no religion”. At the time, campaign groups such as the British Humanist Association claimed that this meant these people were “non-religious”. But is this necessarily the case? Might those who ticked “no religion” be closer to the 46 million adult “nones” in America, who the Pew Research Center described as “religious without religious affiliation”? Or might it be better to characterise these respondents as “a-religious”? This article discusses findings from qualitative research with 14- and 15-year-olds in England who similarly identify as having “no religion”. It describes these young people’s understandings and constructions of “religion”, made during photo-elicitation interviews, as well as their reasons for choosing to tick the “no religion” box in response to the religion question taken from the 2011 Census. Implications are drawn about concepts employed by researchers in the burgeoning field of nonreligion and secularity studies, as well as the methods used by researchers of youth and religion in the social sciences. In particular, questions are raised about value of survey and interview research that asks young people to choose particular self-identities or respond to statements of religious belief and value that may have little relevance to their lives.

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)0.011
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.011
GPT teacher head0.284
Teacher spread0.273 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

Explore more

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