Bibliographic record
Abstract
In many western democracies today, there are calls to strengthen a sense of common citizenship as a way of building ‘social cohesion’ in increasingly diverse societies. Citizenship is to be promoted by, amongst other things, adding or strengthening citizenship education in schools, providing citizenship classes to immigrants, imposing new citizenship tests for naturalization, and holding citizenship ceremonies. In this article, I will examine this new citizenship agenda in the specific case of ‘multination’ states — that is, in states that have restructured themselves to accommodate significant sub-state nationalist movements, usually through some form of territorial devolution, consociational power-sharing, and/or official language status. What does it mean to promote a sense of common citizenship in multination states, and how does the new immigration-focused citizenship agenda relate to older debates on multinationalism? I will argue that in the particular context of multination states, these new citizenship agendas must promote a distinctly multinational conception of citizenship if they are to be fair and effective. But equally, we need to adapt familiar models of multinational citizenship to be more inclusive of immigrants. In short, if the citizenship agenda is to be effective, and to be fairly inclusive of both sub-state national groups and of immigrants, we need a more multinational conception of citizenship, and a more multicultural conception of multinationalism.
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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 | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".