Partisan Manipulation of the Democratic Process and the Comparative Law of Democracy
Bibliographic record
Abstract
This dissertation argues for institutionally focused judicial oversight of the law of democracy in order to minimize partisan manipulation of the democratic process. Elected representatives have incentives to distort for partisan gain campaign finance, political party funding, electoral boundaries, and the other rules shaping democratic political competition. While representatives are elected to act in the best interests of voters, they can entrench themselves and reduce their electoral accountability by altering the rules that structure elections and democratic politics. In other words, political representation is beset by a principal-agent problem. I trace the implications of the principal-agent problem for judicial review of the comparative law of democracy. I argue that leading accounts of judicial review of the law of democracy have failed to account for the presence of electoral management bodies (EMBs), such as election commissions and redistricting commissions, and the role played by majoritarian constitutional structures. If the goal is to reduce partisan self-dealing then judicial oversight must be institutionally sensitive, because the risk of partisan self-dealing varies depending on the type and characteristics of the institution whose actions are under review. I argue that courts should show different degrees of deference based on the risk that the institution is likely to have distorted the democratic process. EMBs are likely to be independent and impartial and the risk of partisan self-dealing by these institutions is therefore reduced. To the extent that they are independent and impartial, then these institutions are entitled to deference. Courts must be aware, however, of the variation in institutional design of EMBs, which range from independent and impartial to partisan and captured. Where the EMB has been captured by partisan interests, then deference is not warranted. Where it is the decisions of legislatures and not EMBs that are under review, then courts should engage in vigilant oversight because of the incentives elected representatives possess to manipulate the democratic process. I develop doctrine to aid courts in limiting partisan self-dealing and assessing the proper degree of deference to grant, including a test to assess the independence and impartiality of EMBs. Where legislative action is under scrutiny, I argue that motive-based judicial review is a way forward for courts to prevent partisan self-dealing. The majoritarian democracies of Canada, Australia, India, and the United States are considered in depth in order to make these claims.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".