Amy H. Liu and Joel Sawat Selway, eds. <i>State Institutions, Civic Associations, and Identity Demands: Regional Movements in Greater Southeast Asia</i>. Ann Arbor: University of Michigan Press, 2024.
Bibliographic record
Abstract
In State Institutions, Civic Associations, and Identity Demands, Amy Liu and Joel Selway aim to cast a new perspective on the sources of ethnic and secessionist mobilization in Southeast Asia.The volume contains a collection of 12 essays by country specialists, bookended by the editors' tightly designed analytical framework in the introduction and conclusion.The last chapter, by Henry Hale, engages with the broad conceptual themes of the book and reflects on some of its empirical findings while offering insights on their generalizability to other similar regions.Regions, rather than ethnic groups, are the main unit of analysis used to compare political mobilization.In their introduction, Liu and Selway argue that focusing on regions helps to better understand what causes mobilization.This approach allows a comparison of explanatory factors across cases where little mobilization occurs and cases where there are strong identitybased movements or other political actions.This makes it possible to overcome some of the selection bias that plagues the ethnic conflict literature, such as selecting groups on the basis of spurious assumptions regarding their political identity or selecting cases exclusively on the basis of their mobilization.Two conditions, according to the editors, appear to be necessary for mobilization-particu larly secessionism-to arise along regional lines: exclusion from state institutions is a strong driver for mobilization when combined with the presence of civic associations that unify a group along ethnic lines.The empirical chapters are organized according to these two main factors, while in the conclusion the editors weigh their relative effect and, when combined, how they help to understand various forms of mobilization.The editors are to be commended for their highly organized, well-defined framework that guides the empirical chapters.All the chapters aim to engage with the shared conceptual
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".