Bibliographic record
Abstract
This book has shown that who wins and who loses in local democracy is shaped in no small part by who votes. If the participation of each of America's racial and ethnic groups were even, we would likely see outcomes that diverged sharply from what we see today. Change would perhaps be most dramatic in mayoral elections, in which up to a third of the elections I examined could have ended with a different winner had turnout been even. But the analysis presented here suggests that city council representation could also be transformed by expanded turnout. If we could greatly increase turnout, we might eliminate almost one-quarter of the underrepresentation of Latinos and Asian Americans on city councils across the country. Finally, there is evidence that turnout is closely linked to the policies that governments pursue. Municipalities with higher turnout spend more on welfare and other redistributive programs favored by minorities and less on areas favored by more advantaged white interests. One implication of this set of findings is obvious: in one context in American democracy, voter turnout matters. At the local level, turnout affects who wins the mayoralty, who occupies the city council, and where local governments spend their money. Turnout is, in short, central to any discussion of local democracy. Many will argue that we already know this and that the importance of turnout is readily visible in almost every political arena. But that is not what empirical studies of the American electorate tend to say.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".