Navigating the limits: electoral management bodies and the struggle against disinformation and foreign interference
Bibliographic record
Abstract
The problem of disinformation and foreign interference in elections has increased significantly in recent years. It creates an uneven playing field that hinders fair competition and informed voting. Electoral disinformation manifests itself in two ways: partisan and procedural. Partisan disinformation targets candidates and voters with false information to influence their voting preferences. In contrast, procedural disinformation seeks to disenfranchise voters or undermine the electoral process. Foreign interference in elections can be defined as any attempt to influence the outcome of an election in another country. Have Electoral Management Bodies (EMBs) implemented effective countermeasures to mitigate these risks? The answer is complex, but no. They face institutional, legal and technical constraints that limit their actions. First, EMBs cannot change electoral laws to make them more resilient against the threat of disinformation and foreign electoral interference. Second, disinformation is usually not criminal and falls outside most legislation, making prosecution difficult. Foreign interference falls beyond national jurisdiction. Third, the actions that EMBs can take are limited by their obligations to be fair and impartial. Fourth, while enhancing content curation on social media platforms would be beneficial, EMBs lack the authority to enforce such measures, and these platforms exercise limited control over the content that is published.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".