Immigrants Serving in Local Government: A Systematic Review and Meta-Analysis of Factors Affecting Candidacy and Election
Bibliographic record
Abstract
Descriptive representation, the extent to which politicians reflect the descriptive characteristics (e.g., ethnicity or gender) of their constituents, has been studied at various scales since it was first introduced in Hanna Pitkin's seminal work several decades ago. In recent years, scholars have also begun to investigate immigrant representation in politics, including at the local, state, and national levels of government. This study evaluates the current research on the factors affecting the election of immigrant candidates to municipal government. In addressing the lack of data-driven reviews in this type of research, the paper employs a scoping review methodological framework. Fifty-six distinct factors are identified as important for immigrants' electoral fortunes. The factors are classified under: Macro-level electoral structures and situational elements, meso-level immigrant group dynamics, and micro-level individual candidate characteristics. The most salient factors are elaborated on, together with a discussion on policy implications and future potential areas of inquiry.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".