MétaCan
Menu
Back to cohort
Record W2920080742

Of Irregular Votes and Robocalls: Resolving Disputed Elections in Canada and New Zealand

2012· article· en· W2920080742 on OpenAlexaboutno aff
Andrew Geddis

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicConflict of Laws and Jurisdiction
Canadian institutionsnot available
Fundersnot available
KeywordsLawmakingPolitical scienceDemocracyConstitutionalismOrder (exchange)Law and economicsJurisdictionLawPoliticsSociologyEconomicsLegislature
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.655

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.034
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0190.008
Scholarly communication0.0110.003
Open science0.0030.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.010
GPT teacher head0.246
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicConflict of Laws and JurisdictionFrench-language works237,207