Safeguarding election management bodies in the age of democratic recession
Bibliographic record
Abstract
There is strong evidence that we have entered into a democratic recessionwhere the quality of democracy is being reversed around the world.As the organisations responsible for running elections, election management bodies (EMBs) are at the fulcrum of the challenge of protecting democracy.This article introduces the special issue on 'Safeguarding Election Management Bodies in the Age of Democratic Recession' which aims to consider the emerging challenges that EMBs are facing, and how they can be best equipped to respond to them.It begins by defining some characteristics of a democratic recession and mapping global trends in democratic quality.It charts global trends in election quality and maps variation in the quality of electoral management worldwide.The article then considers the implications of a democratic recession for EMBs and how international and regional organisations have sought to address these problems.Finally, it introduces articles in the special issue.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".