Electoral integrity resilience: protecting elections during global risks, crises, and emergencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emergency situations caused by natural and technological hazards have often been thought to pose a major threat to democratic practices. This article introduces the concept of electoral integrity resilience as the configuration of actors, resources and properties which enable societies to adapt to an external shock which could damage electoral integrity. The COVID-19 pandemic was a critical case which was thought to pose as a major threat to election quality and democracy worldwide. Although there have been many country-specific studies of the effects of the pandemic, cross-national analysis has been limited due to the unavailability of data. The article uses a new original dataset to identify the properties of polities which had the greatest electoral integrity resilience to the pandemic. The findings point to the importance of overall democratic quality, but also EMB capacity and the availability of multiple methods of voting as key aspects of electoral integrity resilience. These are proposed as key components for investment if countries want to build their electoral integrity resilience ahead of forthcoming crises and emergencies. The article has important lessons for the study and praxis of how future national and global risks can be prepared for – and the construction of resilient institutions.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it