Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Why are some ethnopolitical movements divided while others are relatively unified? A growing literature examines the consequences of internal divisions in ethnopolitical movements – and shows that it matters for a range of conflict outcomes – yet the mechanisms causing such divisions remain poorly understood. Our argument emphasizes competitive dynamics between states and self-determination movements and between rival factions within these movements as key determinants of fragmentation. Drawing from literatures on social movements, contentious politics, and civil war, we situate our argument vis-à-vis three alternative and complementary sets of explanations based on theories emphasizing transnational dimensions, political institutions, and structural factors within ethnopolitical groups. Using an original dataset, we test hypotheses explaining movement fragmentation over time and use a case study of Punjab in India to identify specific causal mechanisms and missing variables. Our findings show some support for three of these theories, suggesting that ethnopolitical movements divide as a result of complex and interactive processes. But our findings also underscore that central to explaining fragmentation dynamics are factors capturing competitive dynamics, including repression, accommodation of movement demands, the turn to violence, and the dynamic and changing nature of ethnopolitical demands.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it