When Is a Country Multinational? Problems with Statistical and Subjective Approaches*
Bibliographic record
Abstract
Abstract Many authors have argued that we should make a clear conceptual distinction between mononational and multinational states. Yet the number of empirical examples they refer to is rather limited. France or Germany are usually seen as mononational, whereas Belgium, Canada, Spain and the UK are considered multinational. How should we classify other cases? Here we can distinguish between (at least) two approaches in the literature: statistical (i.e., whether significant national minorities live within a larger state and, especially, whether they claim self‐government) and subjective (i.e., when citizens feel allegiance to sub‐state national identities). Neither of them, however, helps us to resolve the problem. Is Italy multinational (because it contains a German‐speaking minority)? Is Germany really mononational (in spite of the official recognition of the Danes and the Sorbs in someLänder)? On the other hand, is Switzerland the “most multinational country” (Kymlicka)? Let us assume that there is no definite answer to this dilemma and that it is all a matter of degree. There are probably few (if any) clearly mononational states and few (if any) clearly multinational states. Should we abandon this distinction in favour of other concepts like “plurinationalism” (Keating), “nations‐within‐nations” (Miller), “postnational state” (Abizadeh, Habermas), or “post‐sovereign state” (MacCormick)? The article discusses these issues and, in conclusion, addresses the problem of stability and shared identity “plural” societies.
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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 | 0.158 | 0.332 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".