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Record W2064140797 · doi:10.1037/a0019702

Reactive approach motivation (RAM) for religion.

2010· article· en· W2064140797 on OpenAlexafffund
Ian McGregor, Kyle Nash, Mike Prentice

Bibliographic record

VenueJournal of Personality and Social Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council
KeywordsPsychologyIdealismFeelingSocial psychologyPromotion (chess)Action (physics)Religious orientationScale (ratio)Volition (linguistics)EpistemologyPolitics

Abstract

fetched live from OpenAlex

In 3 experiments, participants reacted with religious zeal to anxious uncertainty threats that have caused reactive approach motivation (RAM) in past research (see McGregor, Nash, Mann, & Phills, 2010, for implicit, explicit, and neural evidence of RAM). In Study 1, results were specific to religious ideals and did not extend to merely superstitious beliefs. Effects were most pronounced among the most anxious and uncertainty-averse participants in Study 1 and among the most approach-motivated participants in Study 2 (i.e., with high Promotion Focus, Behavioral Activation, Action Orientation, and Self-Esteem Scale scores). In Studies 2 and 3, anxious uncertainty threats amplified even the most jingoistic and extreme aspects of religious zeal. In Study 3, reactive religious zeal occurred only among participants who reported feeling disempowered in their everyday goals in life. Results support a RAM view of empowered religious idealism for anxiety management (cf. Armstrong, 2000; Inzlicht, McGregor, Hirsch, & Nash, 2009).

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.149
GPT teacher head0.465
Teacher spread0.316 · 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 designObservational
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

Citations93
Published2010
Admission routes2
Has abstractyes

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