What we think we do (to each other): How personality can bias behavior schemas through the projection of if–then profiles.
Bibliographic record
Abstract
People's knowledge about others includes not only person schemas about the typical traits of others but also behavior schemas about the likely interpersonal consequences of different behaviors. In this article, it is argued that perceiver effects can be interactive at the level of behavior schemas. A person's own personality configuration of if-then responses in social interactions (Mischel & Shoda, 1995) may contribute to that person's beliefs about the meaning and impact of relational behaviors more generally. In consequence, people who experience strong (or weak) responses to behaviors that vary along a particular trait dimension, such as warmth-coldness, may expect others to experience similarly strong (or weak) responses to those same kinds of behaviors. In 3 studies, people who were high in trait communion expected others to respond more strongly to behaviors that varied in warmth-coldness than did people who were low in trait communion, and people who were low in trait agency expected others to respond more strongly to behaviors that varied in assertiveness-unassertiveness than did people who were high in trait agency. Studies 2 and 3 provided evidence that participants' behavior schemas were based on assumptions derived from their own if-then personality profiles.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".