Bibliographic record
Abstract
Abstract This chapter considers “electoral integrity” as a legal concept in general and, in particular, its actual and potential uses in constitutions and election laws. Electoral integrity has been widely cited and applied in the political science literature since the launch of the Electoral Integrity Project and the early scholarship. It has had less purchase in the legal scholarship on comparative constitutional and election law. This chapter considers electoral integrity as a legal concept and identifies relevant features of its definition for its application: (1) it is a standard for evaluating integrity based in actual behavior and practices; (2) it is focused on the electoral cycle rather than the specific event of the election; (3) it is an international rather than a domestic standard; and (4) it is closely tied to procedural definitions of democracy. With this conceptual work in mind, the chapter then traces the role of electoral integrity as a legal concept in constitutional design, the boundaries between election laws and constitutions, and judicial review. The section on judicial review considers two particularly fertile areas for the application of electoral integrity as a legal concept in judicial reasoning: the resolution of disputed elections and review of the constitutionality of voter identification (ID) laws. The chapter concludes by considering some future areas of research if electoral integrity is to be used as a legal concept.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".