Contexts to exploit conflicts - Political opportunity structures for radical right parties in Western Europe
Bibliographic record
Abstract
Radical-Right Parties (RRPs) have seen an unprecedented growth in support across the globe, especially in Western Europe, with a great deal of variation in degrees of success.The variety in levels of support and timing are striking.And yet, similar patterns of political behaviour underlie RRP support.This puzzle is the focus of this study.What explains RRP success across countries and over time?One possible explanation for these differences is the context the parties operate in.Electioneering does not happen in a political vacuum.Every election differs in terms of the core issues that dominate the campaign, how parties navigate these issues, and how firm individuals are in their vote choice.The potential for political parties to establish themselves as electorally viable alternatives is at the very least influenced, if not determined, by these factors.This is especially true for challenger parties like RRPs.With an interest in the ascent of the RRP family in Europe, the focus of this dissertation is the political opportunity structure for RRPs.More than that, this work goes further by not just describing or measuring the political opportunity structure.Rather, I connect macro-level and micro-level determinants of party support.Overall, the central claim of this work is simple: context matters for the individual processes leading to RRP support.The ideological core of RRPs necessitates a specific political setting for them to gain electoral support in an election.The three substantive chapters investigate this with different levels in focus.First is emergence: My dissertation shows that over time, political contexts in Western Europe have become more advantageous for RRP success.This RRP-beneficial political opportunity structure can also result from sudden exogenous shocks, such as the sudden increase in salience of an issue due to external events.This establishing phase is the second aspect I study.As third perspective I take is on the maintenance of RRP support, securing the parties' existence.When an RRP has solidified its voter base, individual patterns of support for these challenger parties becomes similar to the voting behaviour for other parties, thus "normalising" the mechanisms underlying RRP support.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".