Small Worlds in Europe: an exploration of European sub-state party systems
Bibliographic record
Abstract
Throughout Europe there exist many distinct substate party systems which diverge to varying degrees from the state to which they belong, distinguished chiefly by the presence of non-statewide parties (NSWP). These are the ‘Small Worlds’ of this project- a concept first articulated by Elkins and Simeon in their 1980 study of Canada. Small Worlds are multidimensional spaces, where the socio-economic cleavage is joined by salient centre-periphery divisions, and in this, and on the level of support given to NSWP, they demonstrate significant variance across cases. In some NSWPs receive minimal support, and in others statewide parties are not present in the regional arena. And while in some Small Worlds the territorial cleavage is clearly the dominant one, in others the socio-economic divide remains more salient. This research seeks to uncover the causal explanations for the variations in both the extent to which Small Worlds diverge from statewide party systems, and for the relative salience of the territorial cleavage vis a vis the socio-economic cleavage. This will take the form of a two-stage research process. Firstly, a statistical analysis of Small Worlds since 1980, and, secondly, comparative case studies of the Spanish autonomous communities and UK constituent countries, including survey work and data analysis. The research finds that regional economic conditions, historical development, identity and media are key drivers of both factors
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| 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".