Loyal to a fault: Mistaken reputational pressures fuel partisan endorsement of winning at all costs
Bibliographic record
Abstract
In the polarized context of American politics, we study a phenomenon we term winning at all costs: prioritizing ingroup political gain at the expense of immediate societal harm. Do individuals believe the outgroup is willing to win at all costs, and does this impact hostility? Do individuals also think their ingroup is willing to defeat the outgroup at all costs? How well-calibrated are these beliefs, and how do (mis-)perceived reputational pressures shape individuals’ own behavior? Five pre-registered studies (N=6,408) address these questions. Individuals overestimated both outgroup and ingroup willingness to accept societal harm for political gain. (Over)Perceiving outgroup willingness to win at all costs uniquely predicted outgroup animus. And individuals’ mistaken assumptions about reputational rewards from their ingroup increased their endorsement of winning at all costs when their decisions were observable. Our work reveals how an underappreciation for the limits of both ingroup and outgroup partisan psychology reinforces intergroup conflict.
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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 | 0.012 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".