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Record W3028557924 · doi:10.1177/0022022120922312

Beliefs About the Persistence of History in Objects and Spaces in the United States and India

2020· article· en· W3028557924 on OpenAlexaff
Kristan A. Marchak, Merranda McLaughlin, Nicholaus S. Noles, Susan A. Gelman

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPersistence (discontinuity)Consistency (knowledge bases)ParanormalPsychologySocial psychologyVariation (astronomy)Explanatory modelSociologyHistoryEpistemologyMedicineComputer science

Abstract

fetched live from OpenAlex

Scattered evidence in the literature suggests that people may believe that non-visible traces of past events (e.g., origins, emotions, and qualities of the owner) persist over time in objects and spaces, even after the original source has been removed. To date, however, there has been no unified treatment to determine the scope and cultural consistency of this expectation. This study had four primary goals: (a) to assess how broadly participants display persistence-of-history beliefs, (b) to explore individual differences in these beliefs, (c) to examine the explanatory frameworks for these beliefs, and (d) to determine whether these beliefs were endorsed across two cultural settings. Adults in both United States ( N = 195) and India ( N = 173) evaluated a broad range of situations involving possible persistence of history. In both countries, three patterns emerged: (a) A broad range of persistence-of-history scenarios were judged to be possible, falling into two underlying thematic clusters (supernatural vs. non-supernatural); (b) paranormal beliefs predicted endorsement of items in both thematic clusters; and yet (c) most scenarios were explained using natural explanatory frameworks. Together, these results demonstrate broad endorsement of the persistence of history—across cultures, situations, and individuals—as well as substantial individual variation.

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 score0.604
Threshold uncertainty score0.462

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 head0.071
GPT teacher head0.390
Teacher spread0.319 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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