Left Behind and United by Populism? Populism’s Multiple Roots in Feelings of Lacking Societal Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A prominent but underspecified explanation for the rise of populism points to individuals’ feelings of being “left behind” by the development of society. At its core lies the claim that support for populism is driven by the feeling of lacking the societal recognition one deserves. Our contribution builds on the insight that individuals can feel they lack recognition in different ways and for different reasons. We argue that—because of this multifaceted character—the common perception of being neglected by society unites otherwise heterogeneous segments of the population in their support for populism. Relying on data from the German Longitudinal Election Study (GLES) Pre-Election Cross-Section 2021, our preregistered study investigated the multiple roots of populist attitudes in feelings of lacking societal recognition in two steps. First, our results indicate that, from rural residents to sociocultural conservatives or low-income citizens, seemingly unrelated segments of society harbor feelings of lacking recognition, but for distinct reasons. Second, as anticipated, each of the distinct feelings of lacking recognition are associated with populist attitudes. These findings underscore the relevance of seemingly unpolitical factors that are deeply ingrained in the human psyche for understanding current populist sentiment. Overall, by integrating previously disparate perspectives on the rise of populism, the study offers a novel conceptualization of “feeling left behind” and explains how populism can give rise to unusual alliances that cut across traditional cleavages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it