Trends in Voter Surveillance in Western Societies: Privacy Intrusions and Democratic Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it