Electoral Geography, Strategic Mobilization, and Implications for Voter Turnout∗
Bibliographic record
Abstract
When will parties mobilize the electoral support of low-income voters? This discussion presents evidence that rates of turnout among low-income citizens reflect legislators ’ and par-ties ’ electoral incentives to be responsive to the poor, and that these electoral incentives are determined by electoral geography – the joint geographic distribution of legislative seats and low-income voters across electoral districts. Further, this discussion demonstrates that under SMD electoral rules, low-income voters are more likely to vote in those electoral districts in which they are likely to be pivotal. By presenting a strategic mobilization account of voter turnout, this discussion breaks with current accounts of voter turnout that emphasize facilita-tive and motivational individual- and system-level factors. Instead, this discussion argues that low-income voters ’ turnout decisions, in fact, reflect parties ’ electoral incentives to cultivate and mobilize a low-income constituency. ∗This paper was prepared for presentation at the First Annual Toronto Political Behaviour Workshop. This
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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 | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".