‘Citizens of the region’: Party conceptions of regional citizenship and immigrant integration
Bibliographic record
Abstract
Abstract Citizenship is usually regarded as the exclusive domain of the state. However, changes to the structure of states resulting from decentralisation and globalisation have required a re‐conceptualisation of citizenship, as authority is dispersed, identities multiply and political entitlements vary across territorial levels. Decentralisation has endowed regions with control over a wide range of areas relating to welfare entitlements, education and cultural integration that were once controlled by the state. This has created a new form of ‘regional citizenship’ based on rights, participation and membership at the regional level. The question of who does or does not belong to a region has become a highly politicised question. In particular, this article examines stateless nationalist and regionalist parties' (SNRPs) conceptions of citizenship and immigration. Given that citizenship marks a distinction between members and outsiders of a political community, immigration is a key tool for deciding who is allowed to become a citizen. Case study findings on Scotland, Quebec and Catalonia reveal that although SNRPs have advocated civic definitions of the region and welcome immigration as a tool to increase the regional population, some parties have also levied certain conditions on immigrants' full participation in the regional society and political life as a means to protect the minority culture of the region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".