The politics of privacy and the privacy of politics: Parties, elections and voter surveillance in Western democracies
Bibliographic record
Abstract
This paper surveys the various voter surveillance practices currently 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. Five interrelated techniques are analyzed: the development of voter management databases; the use of personal data from commercial data brokerage firms; micro-targeting; the decentralization of data to local campaigns; and “targeted sharing” through social media. Structural and cultural differences between the United States and other democratic countries prevent the extensive and direct export of many of these practices. Yet issues about inappropriate communication from parties, about the sharing of data across systems, about intrusive uses of the Internet and social media, and about data breaches have surfaced in some countries. Furthermore, trends in Western party systems towards a greater de-alignment of the electorate will surely place further 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. Voter surveillance requires further comparative analysis from a variety of disciplinary perspectives. The issues are not confined to the privacy of the individual voter, but relate to broader trends 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".