Strategic Actors' <i>In Situ</i> Impressions of Systematically Versus Unsystematically Variable Counterparts
Bibliographic record
Abstract
The covariation model of attribution holds that when an actor's behavior varies across situations, observers make situational, rather than dispositional, inferences about the actor. We conducted four studies to test the hypothesis that situationally variable behavior can also elicit strong dispositional inferences when the behavior follows a systematic if…then… situation-behavior contingency. In all studies, participants, who believed that they were interacting with another person in a 30-round repeated prisoner's dilemma game, made strong dispositional inferences about counterparts. However, the specific dispositions they inferred depended upon the type of variability the counterpart displayed: positive dispositions (e.g., rational) when the counterpart's behavior followed a systematic (if…then…) pattern that made sense given the context; negative dispositions (e.g., irrational) when the counterpart's behavior was unsystematic, or when the if…then… pattern was inappropriate for the context. Taken together, these studies begin to identify when behaviors that vary across situations improve versus harm perceivers' impressions.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.000 |
| 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".