Threats to personal control fuel similarity attraction
Bibliographic record
Abstract
We propose that experiencing a lack of personal control will increase people’s preferences for self-similar others and that this effect would be explained by a greater need for structure. Our hypotheses received support across 11 longitudinal, experimental, and archival studies composed of data from 60 countries (5 preregistered studies, N = 90,216). In an analysis of cross-country archival data, we found that respondents who indicated a lower sense of personal control were less likely to prefer to live with neighbors who had a different religion, race, or spoke a different language (Study 1). Study 2 found that participants who perceived lower (vs. higher) personal control indicated greater liking for coworkers who they perceived to be more self-similar across a wide range of characteristics (e.g., gender, personality). Studies 3a and 3b, two live-interaction experiments conducted in the United States and China, provided additional causal evidence for control-motivated similarity attraction. A causal experimental chain (Studies 4a to 4c) and a manipulation-of-mediation-as-a-moderator study (Study 5) provided evidence for the mediating effects of the need for structure. Study 6, a longitudinal study with Chinese employees, found that workers who reported perceiving a lower (vs. higher) sense of personal control preferred more self-similar coworkers, and this effect was mediated by a greater desire for structure. Finally, exploring downstream consequences, Studies 7a and 7b found that control-motivated similarity attraction was associated with a greater preference for homogenous (vs. diverse) groups. These findings highlight how the fundamental motive for personal control shapes the structure of social life.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".