Electoral Geography, Strategic Mobilization, and Implications for Voter Turnout∗
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it