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Record W2175179899 · doi:10.1111/jssr.12218

God, Yoga, and Karate

2015· article· en· W2175179899 on OpenAlexaff
Joseph Yi, Daniel Silver

Bibliographic record

VenueJournal for the Scientific Study of Religion · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMartial artsAppealStudioThe artsSociologyKey (lock)AestheticsWhite (mutation)Relation (database)Gender studiesPolitical scienceArtVisual artsLaw

Abstract

fetched live from OpenAlex

We investigate the location patterns of organizations that embody key religious‐spiritual traditions and that have grown to prominence in the latter 20th and early 21st centuries: evangelical churches, yoga, and martial arts. The distribution of key cultural organizations depends on the degree to which they are able to frame themselves in relation to one another and to core American traditions. Organizations associated with the American religious divide are more polarized in their social appeal and spatial distributions, and those framed as broadly neutral elements of popular culture are more widely distributed. Using a national database of local amenities, we find that theologically conservative churches are popular in many neighborhoods but concentrated in less‐educated and nonwhite areas. Yoga studios are less geographically dispersed and more spatially concentrated in college‐educated and white areas. Compared to these, martial arts schools, sports clubs, and other pop‐culture amenities are more widely distributed across different types of areas.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.077
GPT teacher head0.367
Teacher spread0.290 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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