Federalism, Devolution & Secession: From Classical to Post-Conflict Federalism
Bibliographic record
Abstract
Federalism has long been a topic of study for comparative constitutional law. However, the scholarly literature on federalism is in a process of transition. For most of the twentieth century, the study of federalism was oriented around a standard set of cases in the developed world: Australia, Canada, Switzerland and the United States of America. These cases provided the raw material for certain fundamental questions: What is federalism? Why should federations be adopted? What role is there for courts? For the most part, these questions appear to have been answered, often with the aid of comparative analysis. Recent developments in the practice of constitutional design have challenged this consensus. Many states in the developing world, such as Ethiopia, Iraq, Nigeria and Sudan, have adopted federal solutions to manage ethnic conflict, often as part of a broader package of post-conflict constitutional reforms. In these federations, internal boundaries are drawn to ensure that territorially concentrated national minorities constitute regional majorities. The difference between the standard and emerging cases is not just geographic. Rather, the very mission of federalism is different. Its principal goals are not to combat majority tyranny or to provide incentives to states to adopt policies that match their citizens’ preferences, but rather to avoid civil war or secession. Federalism promotes not public accountability or state efficiency but rather peace and territorial integrity. Post-conflict federalism also pursues different goals than classical federalism and thus provides an opportunity to revisit the basic assumptions underlying the field. Advocacy of federalism as a tool for managing ethnic conflict continues to grow, with respect to a diverse set of cases that spans the globe from South and East Asia to Eastern Europe. However, its purported benefits have been challenged by those who argue that federalism exacerbates, instead of mitigates, ethnic conflict. This academic debate about the merits of post-conflict federalism has reached an impasse, largely as a consequence of methodology. Proponents and opponents of drawing boundaries to empower national minorities point to different cases of federal success and failure. But recent scholarship in comparative politics that combines large-n quantitative analysis with small-n qualitative case studies promises a way forward. It shows how we might test these competing claims about the ability of federalism to control ethnic conflict across a variety of cases and begin to identify the factors that explain when post-conflict federalism succeeds and when it does not.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".