Bibliographic record
Abstract
Ethnic minority communities make claims for cultural rights from states in different ways depending on how governments include them in policies and practices of accommodation or assimilation. However, institutional explanations don’t tell the whole story, as individuals and communities also protest, using emotionally compelling narratives about past wrongs to justify their claims for new rights protections. Democratization and Memories of Violence: Ethnic minority rights movements in Mexico, Turkey, and El Salvador examines how ethnic minority communities use memories of state and paramilitary violence to shame states into cooperating with minority cultural agendas such as the right to mother tongue education. Shaming and claiming is a social movement tactic that binds historic violence to contemporary citizenship. Combining theory with empirics, the book accounts for how democratization shapes citizen experiences of interest representation and how memorialization processes challenge state regimes of forgetting at local, state, and international levels. Democratization and Memories of Violence draws on six case studies in Mexico, Turkey, and El Salvador to show how memory-based narratives serve as emotionally salient leverage for marginalized communities to facilitate state consideration of minority rights agendas. This book will be of interest to postgraduates and researchers in comparative politics, development studies, sociology, international studies, peace and conflict studies and area studies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.844 | 0.786 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".