The Populist Radical Right in the US: New Media and the 2018 Arizona Senate Primary
Bibliographic record
Abstract
This article analyzes the appeal of populist radical right (PRR) politics in the US after the election of Donald Trump. Specifically, I seek to explain how new media helps politicians representing the PRR secure support in Republican primaries. Using an online survey of 1052 Arizona Republicans in the lead-up to the August 2018 Senate primary, I evaluate support for three candidates: Rep. Martha McSally, former Maricopa County Sheriff Joe Arpaio, and Kelli Ward, a physician. The findings highlight a bifurcation in the drivers for support of PRR candidacies: Skepticism of immigration drives the Arpaio vote, while use of social media news and belief in party convergence mobilize Ward’s support. The results demonstrate that support for PRR politicians in the Arizona primary is concentrated in two groups, anti-immigrant and anti-establishment, and that the anti-establishment voters are more likely to access news on social media. These findings indicate that social media news consumption does shape voter perceptions about mainstream parties favorably for the PRR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".