State Institutions, Civic Associations, and Identity Demands
Bibliographic record
Abstract
While the media tends to pay the most attention to violent secessionist movements or peaceful independence movements, it is just as important to understand why there are regions where political movements for autonomy fail to develop. In neglecting regions without political movements or full-blown independence demands, theories may be partial at best and incorrect at worst. State Institutions, Civic Associations, and Identity Demands examines over a dozen regions, comparing and contrasting successful cases to abandoned, unsuccessful, or dormant cases. The cases range from successful secession (East Timor, Singapore) and ongoing secessionist movements (Southern Philippines), to internally divided regional movements (Kachin State), low-level regionalist stirrings (Lanna, Taiwan), and local but not regional mobilization of identity (Bali, Minahasan), all the way to failed movements (Bataks, South Maluku) and regions that remain politically inert (East and North Malaysia, Northeast Thailand). While each chapter is written by a country expert, the contributions rely on a range of methods, from comparative historical analysis, to ethnography, field interviews, and data from public opinion surveys. Together, they contribute important new knowledge on little-known cases that nevertheless illuminate the history of regions and ethnic groups in Southeast Asia. Although focused on Southeast Asia, the book identifies the factors that can explain why movements emerge and successfully develop and concludes with a chapter by Henry Hale that illustrates how this can be applied globally.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".