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Record W3109520182 · doi:10.1080/0048721x.2020.1845249

The costs of religious ‘cheap talk’: the semiotics of interpretant driven costly signaling within contested communicative environments

2020· article· en· W3109520182 on OpenAlexaff
Brandon Daniel-Hughes

Bibliographic record

VenueReligion · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsJohn Abbott College
Fundersnot available
KeywordsSemioticsFunction (biology)SolidarityPluralEpistemologySociologyPolitical scienceLinguisticsPhilosophyLaw

Abstract

fetched live from OpenAlex

In recent decades scholars of religion have used costly signaling theory to advance our understanding of seemingly wasteful behaviors by showing how they may function as in-group signals, enhancing social solidarity and cooperation. However, most analyses have assumed that speech and other forms of ‘cheap talk’ like common adornments are too cheap to function as costly signals. This article argues that integrating recent work on the multiple mechanisms of honest signaling and paying closer attention to the semiotic analyses of Charles Peirce, especially his focus on interpretants, allows us better to assess the relationships between signal costs and culturally heterogeneous communicative environments. In religiously contested communicative environments ostensibly cheap signs may function as costly signals. The article develops a theory of interpretant driven costly signaling and lays out the conditions for religious ‘cheap talk’ and inexpensive adornment to function as costly signals in religiously plural communicative environments.

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)0.008
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.051
Scholarly communication0.0090.010
Open science0.0010.005
Research integrity0.0030.004
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 head0.025
GPT teacher head0.308
Teacher spread0.283 · 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
Published2020
Admission routes1
Has abstractyes

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