Left behind and united by populism? Populism’s multiple roots in feelings of lacking societal recognition
Bibliographic record
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 to lack recognition in different ways and for different reasons. We argue that—due to this multifaceted character—the common perception of being neglected societal recognition unites otherwise heterogeneous segments of the population in their support for populism. Relying on data from the GLES Pre-Election Cross-Section 2021, our pre-registered 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 socio-cultural 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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".