How inferred contagion biases dispositional judgments of others
Bibliographic record
Abstract
Abstract Drawing on recent evidence suggesting that beliefs about contagion underlie the market for celebrity‐contaminated objects, the current work investigates how people can make biased dispositional judgments about consumers who own such objects. Results from four experiments indicate that when a consumer comes in contact with a celebrity‐contaminated object and behaves in a manner that is inconsistent with the traits associated with that celebrity, people tend to make more extreme judgments of them. For instance, if the celebrity excels at a particular task, but the target who has come into contact with the celebrity‐contaminated object performs poorly, people reflect more harshly on the target. This occurs because observers implicitly expect that a consumer will behave in a way that is consistent with the traits associated with the source of contamination. Consistent with the law of contagion, these expectations only emerge when contact occurs. Our findings suggest that owning celebrity‐contaminated objects signals information about how one might behave in the future, which consequently has social implications for consumers who own such objects.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".