A Social Identity Approach to How Elite Outgroups Are Invoked by Politicians and the Media in Nativist Populism
Bibliographic record
Abstract
Existing research into nativist populist (NP) rhetoric has shown that elite outgroups can be used by politicians to further anti‐immigration agendas. The social identity functions of elite outgroups outside of cultivating anti‐immigrant prejudice, however, remain poorly understood. In addition, whether populist news media can be considered social identity entrepreneurs in their own right remains an underexplored topic. This study examines the rhetorical use of elite outgroups in the United Kingdom, United States, and Australia from a social identity perspective, focusing on political leaders and newspapers op‐eds. Our findings demonstrate shared strategies across the countries and source types: (1) NPs depict elites as working through collusion to undermine trust in information production within society and vie for control of the ingroup informational influence; (2) NPs present themselves as nonelite and more ingroup prototypical on dimensions relevant to the elite collusion (being under attack and equally susceptible); (3) NPs contest ingroup norms through constructions of an anti‐immigrant consensus which is suppressed by elites. We conclude that social identity researchers should pay more attention to the rhetorical functions of elite outgroups in addition to cultivating anti‐immigrant prejudice, and that the media‐as‐identity‐entrepreneur is an important aspect of constructing shared social realities, and mobilizing support, within populism.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".