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Record W3205923260 · doi:10.3390/rel12100867

The Natural Pathways to Atheism: Cognitive Biases, Cultures, and Costs

2021· article· en· W3205923260 on OpenAlexaff
Brandon Daniel-Hughes

Bibliographic record

VenueReligions · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsJohn Abbott College
Fundersnot available
KeywordsAtheismNatural (archaeology)EpistemologyScholarshipCognitive science of religionSociologyPhilosophyEnvironmental ethicsCognitionPsychologyHistoryLawPolitical science

Abstract

fetched live from OpenAlex

Some recent scholarship in the bio-cultural sciences of religion has argued that atheism, like science and doctrinal theology, is less natural than religion. This scholarship, however, draws on problematic natural/unnatural and nature/culture binaries that denaturalize culture and reify a more basic essence/accident binary. Here, I argue that (1) while the suggestion that religion is more natural than atheism indicates something important, it reinforces assumptions about the naturalness of cognition and the unnaturalness of culture that confuse as much as they explain; (2) a clearer understanding of atheism requires the thorough naturalization of culture; (3) multiple pathways to atheism can then be understood as natural developments of both cognitive and cultural predispositions, and analyzed along continua of religion-reinforcing cultural scaffolding and religion-fostering cognitive intuitions; and (4) finally, I suggest an economic frame for better understanding atheist expressions that construes atheism, despite its relative costs and rarity, as a natural though expensive phenomenon. Because atheist expressions are differentiated by the mechanisms (cultural and cognitive) they utilize to pay the costs of overriding religion-fostering intuitions and religion-reinforcing cultural scaffolding, all atheist expressions are naturalized along with culture; however, the basic insight, indicated by the claim that religion is more natural than atheism, is preserved.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.309
Teacher spread0.288 · 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 designTheoretical or conceptual
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

Citations1
Published2021
Admission routes1
Has abstractyes

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