Does the 1 Person 1 Vote Principle Apply?
Bibliographic record
Abstract
Abstract In this essay we address the puzzle that exists in American politics based on the tension of convergence to the electoral mean because of the MVT (mean voter theorem) and the studies showing divergence in candidate positioning. We provide a model in which voters and states are not treated equally because of vast regional differences. In contrast with the MVT, candidates who campaign in each state may converge to the national electoral mean while adopting diverging positions in different states, as they take differences in voter preferences and valences across states into account. At the state level, we show that while candidates give maximal weight in their policy position to pivotal voters, they give minimal weight to those voting for them with almost certainty; and that in their national position while candidates give maximal weight to swing states they give minimal weight to nonpivotal states. Something that remains hidden when differences across states are ruled out as they are in MVT. Then we adapt the variable choice set logit model of Gallego et al . (2013) to study the 2008 Presidential election and find that even though Obama's and McCain's position in swing states differs from the national electoral mean, their national position are close but on opposite sides of the national mean. Given the differential treatment candidates give voters and states in their national position, incorporating the Electoral College vote in the model, the “one person, one vote” principle may fail to obtain in the 2008 US Presidential election when candidates' valences and campaign spending differ across 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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".