Exposure to robot preachers undermines religious commitment.
Why this work is in the frame
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Bibliographic record
Abstract
Over the last decade, robots continue to infiltrate the workforce, permeating occupations that once seemed immune to automation. This process seems to be inevitable because robots have ever-expanding capabilities. However, drawing from theories of cultural evolution and social learning, we propose that robots may have limited influence in domains that require high degrees of "credibility"; here we focus on the automation of religious preachers as one such domain. Using a natural experiment in a recently automated Buddhist temple (Study 1) and a fully randomized experiment in a Taoist temple (Study 2), we consistently show that religious adherents perceive robot preachers-and the institutions which employ them-as less credible than human preachers. This lack of credibility explains reductions in religious commitment after people listen to robot (vs. human) preachers deliver sermons. Study 3 conceptually replicates this finding in an online experiment and suggests that religious elites require perceived minds (agency and patiency) to be credible, which is partly why robot preachers inspire less credibility than humans. Our studies support cultural evolutionary theories of religion and suggest that escalating religious automation may induce religious decline. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it