The Multiple Nontransferable Vote in Theory and Practice: Dynamic Political Behavior in New Hampshire and Vancouver
Bibliographic record
Abstract
A great deal of academic attention has been paid to both the majoritarian elections of single winners and the proportional election of multiple winners. Research has included the study of the connection of these systems to issues varying from voter behavior, party formation and stability, equity, social justice, ethnic tensions, and polarization. Nonetheless, the intersection of these systems---the majoritarian election of multiple winners---has escaped any considerable academic attention, at both the theoretical and empirical levels. This dissertation makes a major step forward into this research void by examining the most simple and common form of majoritarian multiple-winner election: the multiple nontransferable vote (MNTV), in which M candidates are elected, each voter can vote for up to M candidates, and the M candidates with the most votes win.Through three papers, compiled herein as three substantive chapters along with an introduction and a conclusion, this dissertation builds and then tests three theories related to the ability of political actors to react and behave strategically in political systems that employ MNTV. Aside from the focus on the same electoral system, the common thread throughout each chapter are the questions: Does MNTV present unique strategic considerations, distinct from those of single-winner majoritarian and proportional systems? Do candidates, parties, and voters understand these incentives well enough to act strategically? Finally, do these individual strategic considerations result in any unique emergent properties in the political systems that employ MNTV?To answer these broad questions, each substantive chapter poses a more specific question regarding strategic behavior in MNTV systems. Chapter 2 finds that voters will vote more strategically when their preferred-party's control of the legislature is threatened in New Hampshire's districted MNTV system. Chapter 3 finds that candidates and parties are more likely to enter Vancouver's MNTV system when the balance of power between the parties presents a strategic opportunity for them to do so. Chapter 4 explores the limits of actors' ability to strategically act in MNTV systems by discovering a period of system shock defined by erratic behavior following a modification to Vancouver's electoral system in 1968. Together, these chapters present a set of strategic incentives that are unique to systems that use MNTV to elect their leaders and then show that actors have a surprising (albeit limited) capability to respond to these incentives. These findings advance the study of electoral systems in a new direction and have normative implications regarding the reform and equity of MNTV systems.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".