Strength of Social Cues in Online Impression Formation
Bibliographic record
Abstract
The social identity model of deindividuation effects (SIDE) predicts individuals in depersonalized settings associate with those with whom they share a salient social identity and disassociate from others. We challenge the strict ingroup/outgroup bifurcation used in prior research and posit that ingroup perceptions differ across distinct (i.e., moderate and extreme) outgroups. A 2 (high cues vs. low cues) × 3 (ingroup, moderate outgroup, extreme outgroup affiliation) experiment utilized 128 subjects to examine how members of an ingroup view individuals belonging to various outgroups. Findings expand SIDE research by addressing the interaction between the valence of social cues to a social group and the strength of those cues. The interaction demonstrates that ingroup members with stronger social cues are more socially identifiable than ingroup members who provided few cues to their ingroup membership, while extreme outgroup members who minimize cues to their identity are more socially identifiable to ingroup members than outgroup members who provide numerous cues.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".