Antecedents and Consequences of Evaluative Concerns Experienced During Intergroup Interaction: When and How Does Group Status Matter?
Bibliographic record
Abstract
Individuals often evidence substantial egocentrism by virtue of being preoccupied with themselves and how they appear to others during social interaction. For example, people overestimate the extent to which others’ behavior is caused by and directed toward them personally (Zuckerman et al., 1983), perceive that others pay more attention to them than is actually the case (Gilovich, Medvec, & Savitsky, 2000), and even exaggerate others’ focus on their failures and mishaps (Savitsky, Epley, & Gilovich, 2001). Such egocentric biases may sometimes seem counterintuitive, as when they lead individuals to reach overly pessimistic conclusions about how they are viewed. However, the biases are readily understandable through the lens of contemporary theories emphasizing how individuals’ fundamental motivation to maintain a sense of belonging and acceptance leads them to continuously monitor their social standing with others (e.g., Leary & Downs, 1995). Critical from an intergroup relations perspective is that individuals do not leave this motivation for social acceptance – or the egocentric biases that it fosters – behind when they enter intergroup interaction situations. Although myriad forces are of course operative in these settings, individuals’ basic human desire to understand and manage their social standing with others remains very much in play. What does this mean for intergroup relations? The information search model (Vorauer, 2006) drew on existing research and theory to make a range of predictions regarding antecedents and consequences of evaluative concerns during intergroup interaction. A key premise of the model was that these concerns could have a variety of negative implications for intergroup relations. Although at the time clear evidence supported of some of the model's propositions, other propositions were largely speculative. In the decade or so since then, research has been conducted that speaks to many of these originally untested, or sparsely tested, ideas, and more data from members of minority or lower-status groups are available now than was initially the case. Accordingly, we review the new research and assess the extent to which the findings support versus contradict key propositions of the model. Because of space limitations, we focus on group memberships defined by ethnic background and broad themes, especially with respect to consequences. We place particular emphasis on the evidence for similarities versus differences across members of higher- and lower-status groups. We conclude by considering implications for intervention and identifying what we see as important next steps for research in this area.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".