Beliefs About the Persistence of History in Objects and Spaces in the United States and India
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".