Bibliographic record
Abstract
Abstract Elections matter. Nearly all countries around the world use elections as a means to legitimize the power of the government in office. However, they often fail to contribute to the realization of democracy through flaws such as gerrymandered electoral districts, electoral violence, or the spread of disinformation. The Oxford Handbook of Electoral Integrity seeks to consolidate research in the field of electoral integrity, ground new concepts, and set the research agenda for future research. This introduction frames the volume by providing a historical genealogy of how the study of electoral integrity has evolved over time. It draws from that literature four ways in which “good” elections have been defined and sets out an approach to defining electoral integrity used in the volume based around democratic theory. The Introduction sets out the full range of research traditions which now exist in the maturing field of electoral integrity. It also summarizes the approach taken to compile this volume—and provides a roadmap for the book ahead. The study of electoral integrity must be normatively anchored, empirical, policy-driven, methodologically diverse, and enable global inclusive conversations.
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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 | 0.000 |
| 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 | 0.000 |
| 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".