The impact of the cultural backlash on the rise of european national populism : the fear of ethnic disappearance : Cross-Country Study of Austria and Hungary
Bibliographic record
Abstract
National Populists are winning seats around Europe and liberal democracy finds itself increasingly undermined. According to the report Democracy in Crisis (2020)1 , the number of countries that have suffered democratic setbacks surpasses the number having achieved gains in this realm. This dissertation for the MA Program in Governance, Leadership and Democracy Studies will reflect on how the cultural backlash is fueling white identity resentments through the fear of ethnic change. According to Kaufmann (2018), in order to understand the phenomenon of national populism, the issue of migration “is central and ethnic change is the story.” (Kaufmann 2018,11). According to Goodwin and Eatwell (2018) the national populist phenomenon reflects deeprooted fears about immigration and hyper ethnic change (Goodwin and Eatwell 2018,132). People feel instinctively negative about how immigration could threaten their national identity, and consequently lead to the destruction of the wider group identity, calling into question the future of the white majority (Taub 2016; Kaufmann 2018; Eatwell and Goodwin 2018).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".