The Affective Structure of Stereotype Content
Bibliographic record
Abstract
Affect control theory and the stereotype content model share explanatory goals and employ compatible measurement strategies but have developed in largely separate literatures. The present article examines the models’ commensurability and discusses new insights that can be gained by comparing theories. We first demonstrate that the unique measurement dimensions used by each theory (evaluation/potency/activity vs. warmth/competence) describe much of the same semantic content. We then show how simulation techniques developed by affect control theorists can be applied to the study of interactions with stereotyped groups. These simulations indicate broad consistencies between the theories’ predictions but highlight three distinctive emphases of affect control theory. Specifically, affect control models predict that actors are motivated to behave in ways that (1) are consistent with self-meanings, (2) maintain cultural norms about the suitability of behaviors and emotions to role relations, and (3) account for behavior and emotion in prior interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".