Cyber Elections in the Digital Age: Threats and Opportunities of Technology for Electoral Integrity
Bibliographic record
Abstract
Elections are essential for delivering democratic rule, in which ultimate power should reside in the citizens of a state. This introduction argues that the management and contestation of elections have now entered a qualitative new historical period because of the combined development of new technology and broader sociological developments. The era of cyber-elections is marked by: (a) the new ontological existence of the digital, (b) new flows of data and communication, (c) the rapid acceleration of pace in communications, (d) the commodification of electoral data, and (e) an expansion of actors involved in elections. These provide opportunities for state actors to incorporate technology into the electoral process to make democratic goals more realizable. But it also poses major threats to the running of elections as the activities of actors and potential mismanagement of the electoral process could undermine democratic ideals such as political equality and popular control of government. The article argues that this new era therefore requires proactive interventions into electoral law and the rewriting of international standards to keep pace with societal and technological change.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".