<b>Conference Brief. </b>Executive Overreach in the Electoral Arena<b>: </b>Summary of Findings from an Expert Roundtable
Bibliographic record
Abstract
On May 12, 2025, the University of Notre Dame’s Kellogg Institute for International Studies hosted a panel focusing on the role of electoral institutions in safeguarding democracy against executive encroachment, as part of the Institute’s Global Democracy Conference (GDC). Chaired by David Campbell (University of Notre Dame), the panel brought together Laura Gamboa (University of Notre Dame), Holly Ann Garnett (Royal Military College of Canada), Ugur Ozdemir (University of Edinburgh), and S. Y. Quraishi (former Chief Election Commissioner of India). Drawing on comparative and first-hand experience, the panelists analyzed how executives undermine electoral integrity, and discussed the conditions and strategies that enable political parties and electoral management bodies (EMBs) to resist authoritarian pressure. This brief synthesizes the panel’s insights and broader conference discussions to offer actionable recommendations for democratic actors and donors.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.019 |
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".