Politicizing Social Inequality: Competing Narratives From the Alternative for Germany and Left-Wing Movement Stand Up
Bibliographic record
Abstract
This article investigates the link between rising levels of social inequality and years of austerity on the one hand and the rise of populist, anti-establishment protest on the other. This connection is explored by analyzing the discursive practices of activists as a way of reconstructing the key argumentative and emotional structures organizing actors’ understanding of politics. Empirically the article is based on 40 narrative interviews with supporters of the German right-wing, anti-immigrant party, Alternative for Germany (AfD), and the newly established left-wing movement Stand Up. The findings of the discursive analysis point to a profound sense of exclusion amongst left- and right-wing populist affiliates defined both in socio-economic terms and with a view to being deprived of a proper political voice. At the same time, the results show that the supporters of the AfD, in contrast to those from Stand Up, develop a strong, mobilizing collective identity that is instrumental in popularizing their discontent with the political establishment: The dramatized conflict between the virtuous German people and the threatening Other - manifested primarily by immigrants and the European Union - provides an emotionally charged binary that is at the core of the contemporary populist resurgence across Western democracies. In addition, the collective identity is instrumental in offering a particular interpretation of the origins of and desirable response to growing inequality that rely more on culturalist rather than traditional class-based arguments. Building on this analysis, the article offers an interpretation of the relative weakness of the populist left that, in the German context, so far has not succeeded in using deepening socio-economic cleavages for their political mobilization effectively.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".