Motivated Categories: Social Structures Shape the Construction of Social Categories Through Attentional Mechanisms
Bibliographic record
Abstract
Social categorization is often framed as the antecedent to stereotyping, with perceivers rationally sorting the social world on the basis of perceptually salient categories before applying biased or motivated beliefs about those categories. Here, we instead suggest that the construction of social categories by individuals is itself subject to motivational influences, such that perceivers will attend to a given dimension of social categorization (e.g., race or gender) insofar as doing so fits within their motivations. Drawing from classic conceptualizations of social structure as the interplay of schemas and resources, we focus on how the motivations for shared schemas and for material benefits or resources may shape attention to social category dimensions. We outline the potential cognitive mechanisms through which these motivations may act on attention, before discussing the implications of this model for individual differences, conceptualizations of social categorization as rational information reduction, and prejudice reduction. Public Abstract Social categories like race and gender often give rise to stereotypes and prejudice, and a great deal of research has focused on how motivations influence these biased beliefs. Here, we focus on potential biases in how these categories are even formed in the first place, suggesting that motivations can influence the very categories people use to group others. We propose that motivations to share schemas with other people and to gain resources shape people’s attention to dimensions like race, gender, and age in different contexts. Specifically, people will pay attention to dimensions to the degree that the conclusions produced from using those dimensions align with their motivations. Overall, we suggest that simply examining the downstream effects of social categorization like stereotyping and prejudice is not enough, and that research should look earlier in the process at how and when we form the categories on which those stereotypes are based.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".