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Record W4308752168 · doi:10.1002/jcpy.1331

The spiritual contagion scale: A measure of beliefs in the transfer of metaphysical properties

2022· article· en· W4308752168 on OpenAlexaff
Jin Kim, George E. Newman, Natalie O. Fedotova, Paul Rozin

Bibliographic record

VenueJournal of Consumer Psychology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsModerationPsychologyDisgustScale (ratio)Construct (python library)Discriminant validitySocial psychologyMetaphysicsCognitive psychologyEpistemologyDevelopmental psychologyPsychometricsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract Contagion is the belief that an entity's invisible or essential qualities can be transferred to a target. Researchers studying contagion have often distinguished between physical contagion (the perceived transfer of germs, toxins, and pathogens) and spiritual contagion (the perceived transfer of metaphysical properties such as spirits, essence, and moral characteristics). While sensitivity to physical contagion is a component of several existing scales, to date, there are no scales that measure sensitivity to spiritual contagion. Here, we develop and validate a measure of Spiritual Contagion Sensitivity (SCS), which considers positive, negative, and neutral aspects of spiritual contagion. We demonstrate discriminant validity from existing measures of physical contagion sensitivity, such as perceived vulnerability to disease and disgust sensitivity (Study 1). We demonstrate construct validity by showing the correlation between SCS and a variety of published findings in the literature for which spiritual contagion beliefs have been hypothesized to play a role (Study 2). Finally, we demonstrate predictive utility of the SCS scale by showing significant moderation of spiritual contagion effects from the literature (Studies 3A–3C).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.120
GPT teacher head0.306
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2022
Admission routes1
Has abstractyes

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