Bibliographic record
Abstract
Introduction - Multinational Federalism: Questions and Queries M.Seymour & A-G.Gagnon PART I: THEORETICAL MATTERS Multinational Federalism in Multinational Federation M.Burgess Three Theories of Liberalism for the Three Theories of Federalism. A Hegelian Turn F.Requejo What Theorists of Nationalism have to Learn from Multinational States P. Resnick PART II: THE EUROPEAN EXPERIMENT The European Union, a Plurinational Federation in Sensu Cosmopolitico H.Dumont Reforging the Nation. Britain, Scotland and the Crisis of Unionism M.Keating The United Kingdom's Experiment in Asymmetric Autonomy, and the Lessons Learned J.McGarry Does Devolution Foster Separatism? The Case of Catalonia M.Guibernau Politics and Public Sphere in Belgium. What the Case of a Federal Multilingual Country can Contribute to the Debate on Transnational Public Spheres, and Vice Versa D.Sinardet PART III: OTHER CASE STUDIES Asymmetry and Change in the Canadian Federation: The Limits of Political Accommodation in a Multination State R.Iacovino & J.Erk The Consequences of Drafting Constitutions for Constituent States in Federal Countries J.Dinan Should Indian Federalism Be Called Multinational? R.Bhargava Conclusion M.Seymour & A-G.Gagnon
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".