Multinational Democratic Federations: Comparing India with Multi-level Systems from the Global North
Bibliographic record
Abstract
In this article, we compare the Indian experience with that of some of the multinational and multi-level polities from the Global North, namely Belgium, Canada, Spain and the United Kingdom. We first summarize the essence of multinationalism. Drawing from our comparative examples of the Global North we then show how dominant narratives of state nationalism condition the extent to which the state can accommodate plurinational difference through self-rule, shared rule and ethno-symbolic recognition within these states, and then compare and contrast this with the Indian experience. Despite the stickiness of elite narratives on the meaning of the state during state formation and democratization, we highlight the ability of electoral competition to push multi-level politics into a more accommodative or majoritarian direction. We illustrate this with reference to India including the 2024 General Election Outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.000 | 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".