Review of “Ethnopolitical Entrepreneurs: Outsiders Inside Armenian Los Angeles”
Bibliographic record
Abstract
Armenians constitute approximately 40% of Glendale, California’s population, yet they hold nearly 70% of the city’s elected positions, including those of mayor, city council members, and school board members. In his book, Ethnopolitical Entrepreneurs, Daniel Fittante explores how this diverse and often fragmented ethnic group has managed to secure such significant political representation and influence in a relatively short time. Through a nuanced examination of local dynamics and the roles played by key individuals, Fittante offers a model that sheds light on political outcomes not only in Glendale but also in other contexts and ethnic communities. In an engaging introduction followed by six insightful chapters, Fittante delves into the Armenian community of Glendale, asking: How did they achieve political mobilization and representation despite being recent immigrants with limited experience in democratic institutions? What roles do local ethnopolitical entrepreneurs play in this process? And how does the Glendale experience reflect broader trends in immigrant political incorporation across the United States?
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".