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Record W4403467446 · doi:10.1080/00224545.2024.2413500

Don’t be rash: how effort, religion, and decision-type influence judgments of morality

2024· article· en· W4403467446 on OpenAlexaff
Veronica N. Z. Bergstrom, Jeffrey S. Robinson, Aisling Curtin Wach, Alison L. Chasteen

Bibliographic record

VenueThe Journal of Social Psychology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMoralityRashSocial psychologyPsychologyEpistemologyPhilosophyMedicineDermatology

Abstract

fetched live from OpenAlex

The present study explored how knowledge (Study 1) and inferences (Study 2) about religiosity influence impressions of morality depending on whether effort is exerted to reach a morally controversial decision. In Study 1, undergraduates judged a [religious/nonreligious] doctor who exerted [little/great] effort into their decision to euthanize a patient. Results indicated that when the doctor was nonreligious or exerted low effort, they were considered less moral compared to when they were religious or exerted high effort. In Study 2, Turk Prime participants evaluated a doctor who decided in favor or against euthanizing a patient, with the same effort manipulation as Study 1. Results indicated that the doctor who favored euthanasia was considered less religious than the doctor who did not. As in Study 1, greater morality was associated with the doctor who exerted greater effort, particularly when they favored euthanasia. When the doctor favored euthanasia, they were rated as more moral when their background was inferred to be more religious; however, the opposite pattern of results emerged when the doctor decided against euthanasia.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.322

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 science0.0000.000
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 head0.086
GPT teacher head0.362
Teacher spread0.276 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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