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Record W2774348690 · doi:10.1177/0008429817741448

Knowledge as Explanandum: Disentangling Lay and Professional Perspectives on Science and Religion

2017· article· en· W2774348690 on OpenAlexvenueno aff
Tom Kaden, Stephen Jones, Rebecca Catto, Fern Elsdon‐Baker

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsnot available
FundersJohn Templeton Foundation
KeywordsFraming (construction)EpistemologyNeglectSociologySociology of scientific knowledgeReinterpretationNormativeAtheismEngineering ethicsPsychologySocial sciencePhilosophyEngineering

Abstract

fetched live from OpenAlex

Issues pertaining to the relationship between science and religion, like creationism, Intelligent Design, and New Atheism, are increasingly the focus of social scientific research. This research often does not differentiate clearly between different kinds of social actors. At the most basic level, professional developers and distributors of systems of thought that deal with the relationship between science and religion, and laypeople who take up this knowledge, or parts of it, must be distinguished. Based upon interview material from the large, multinational study Science and Religion: Exploring the Spectrum, we identify five typical dimensions of lay knowledge vis-à-vis professional knowledge: reinterpretation of professional labels; neglect of important parts of knowledge systems; addition of knowledge; lower ascription of relevance; and an individual ethical framing.

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)0.030
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.056
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0050.059
Scholarly communication0.0150.020
Open science0.0020.012
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.102
GPT teacher head0.495
Teacher spread0.392 · 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 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

Citations36
Published2017
Admission routes1
Has abstractyes

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