Political parties and voter privacy: Australia, Canada, the United Kingdom, and United States in comparative perspective
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
Political parties are among the most lax, unregulated organizations handling large volumes of personally identifiable data about citizens’ behavior and attitudes. We analyze the privacy practices of political parties in Australia, Canada, United Kingdom, and United States to assess the current state of electorate data, compare regulatory efforts, and offer policy recommendations. While data has long been a part of political practice, there has been a revolution over the last decade in the opportunities for gathering, storing, and acting upon data. Candidates, parties, lobby groups and data–mining firms collect massive amounts of data. They trade analytical tools, databases, and consulting expertise on a vast and unregulated market. In these practices, political actors routinely violate the privacy norms of many citizens. There are also documented cases of data breeches in all four countries. Meanwhile, political parties face relatively few restrictions on their use of data, and have developed a wide variety of largely voluntary privacy policies that are inadequate. We argue that some straightforward policy oversight would significantly improve the way personal records are handled by political actors.
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 | 0.000 | 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 | 0.000 | 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