When Establishment and Social Movements Fail: Exploring the Populist Candidacies of the 2016 American Presidential Primaries
Bibliographic record
Abstract
This paper takes a second look at the 2016 American Presidential Primaries from the perspective of asking what the American people were really after then they chose to ultimately support populism. Media reports and editorial discussions all pointed to a base that was somehow backing misogyny and racism. My research points to an alternative. Populism and social movement theory suggest that the success of anti-establishment candidacies is not credited to populists alone; in the case of 2016, it had support in the credibility and political opportunity left by social movements past. And so to investigate this historic battle between the establishment and anti-establishment candidacies, we can look to what populism and social movements have in common, and how they merge during the framing process. Within this context, this research seeks to answer how the anti-establishment candidates of the 2016 American Primaries framed a battle against 'the establishment' – within an establishment arena and won.\nBy seeing populism as more of a logic as opposed to an ideology, we can eliminate a partisan lens in the study of what happened in 2016. By seeing populism as a strategy when added to a collective action frame of a social movement, we can analyze how it was used to mobilize voters to action. By using populism and social movement theory, we can add further context to what voters were experiencing in 2016. This study uses a populist master frame analysis of the 2016 U.S. Presidential Primary debates, which ultimately illustrates the efficacy of populist messaging while exposing the weaknesses of the establishment rhetorical response. Findings suggest that populist candidates unearthed deep insecurities in 2016, specifically in areas concerning the economy, foreign policy, and within the identities affected by loss, discrimination and threats to human rights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".