Of Irregular Votes and Robocalls: Resolving Disputed Elections in Canada and New Zealand
Bibliographic record
Abstract
This paper begins with the broader question of how a constitutional order based upon a liberal-democratic commitment to letting the people choose their lawmakers ought to respond to allegations of flaws in its election process. After all, any large-scale human undertaking is bound to fall short of perfect implementation, so why do such claims matter so much? And if such claims do matter so much, what are the various issues that need to be resolved in order that they may be properly confronted and settled? From this general discussion, the paper then turns to examine how these issues are addressed in two nations that enjoy similar historical, cultural and constitutional traditions: Canada and New Zealand. The point of this comparison is not to illustrate the breadth of all possible responses to the challenge that a disputed election poses to a liberal democratic constitutional order, but rather to demonstrate that even relatively small differences in legal doctrine can have important real-world consequences. Furthermore, it is argued that such differences as can be discerned between the two nations are attributable to the balance each has struck between the perceived need for ensuring procedural correctness and bringing closure to the election process so as to permit elected representatives to carry out their lawmaking functions. Insofar as both of these goals emerges from the model of liberal democratic constitutionalism itself, each jurisdiction’s choices illustrate that any legal response to the challenge of disputed elections is not necessarily “required” but rather the result of a conscious preference for one over the other.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".