The first-member heuristic: Group members labeled “first” influence judgment and treatment of groups.
Bibliographic record
Abstract
= 1,929) tested the hypothesis that people will expect the performance of an arbitrarily ordered group to match that of the group member in the first position of a sequence more closely than that of group members in other positions. This greater perceived diagnosticity of the first member will in turn affect how people treat the group. This pattern of judgment and treatment of groups, labeled the "first-member heuristic," generalized across various performance contexts (e.g., gymnastic routine, relay race, and job performance), and regardless of whether the focal member performed poorly or well (Studies 1-3). Consistent with the notion that first members are deemed most informative, participants were more likely to turn to the member in the first (vs. other) position to learn about the group (Study 4). Further, through their disproportionate influence on the expected performance of other group members, first members' performances also influenced participants' support for policies that would benefit or hurt a group (Study 5) and their likelihood to join a group (Study 6). Finally, perceived group homogeneity moderated the first-member heuristic, such that it attenuated for nonhomogeneous groups (Study 7). (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".