Trends in Voter Surveillance in Western Societies: Privacy Intrusions and Democratic Implications
Bibliographic record
Abstract
This paper surveys the various voter surveillance practices recently observed in the United States, assesses the extent to which they have been adopted in other democratic countries, and discusses the broad implications for privacy and democracy. Four broad trends are discussed: the move from voter management databases to integrated voter management platforms; the shift from mass-messaging to micro-targeting employing personal data from commercial data brokerage firms; the analysis of social media and the social graph; and the decentralization of data to local campaigns through mobile applications. The de-alignment of the electorate in most Western societies has placed pressures on parties to target voters outside their traditional bases, and to find new, cheaper, and potentially more intrusive, ways to influence their political behavior. This paper builds on previous research to consider the theoretical tensions between concerns for excessive surveillance, and the broad democratic responsibility of parties to mobilize voters and increase political engagement. These issues have been insufficiently studied in the surveillance literature. They are not just confined to the privacy of the individual voter, but relate to broader dynamics in democratic politics.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".