How Much Surveillance is Too Much? Some Thoughts on Surveillance, Democracy, and the Political Value of Privacy
Bibliographic record
Abstract
Over the past decade it has become increasingly common to speak of the emergence of a surveillance society. Surveillance is an almost inescapable part of 21st century life. There is a very real danger that individual privacy - as it is currently understood - may soon become a thing of the past. Some would argue privacy is already dead and we have no choice but to accept our newly transparent lives. For many, surveillance has become part of daily life during visit banks, stores, shopping malls, and many public streets and parks. Travel through airports subjects our bodies to physical scans and our passports to electronic scrutiny. Attendees of public gatherings and demonstrations are often overtly captured on police CCTV video cameras. Perhaps the most profound expansion of surveillance has been in the area of dataveillance. Both the state and the private sector routinely require individuals to hand over large amounts of personal information as a matter of law or in exchange for access to services. The private sector owns vast amounts of information about us and processes and shares that information to create consumer profiles capable of accurately predicting individual consumption preferences. The intrusion of surveillance and dataveillance into every aspect of our lives hampers the degree to which individuals are able to construct and control different context-specific social identities. Our ability to function in society rests partly on our ability to keep these social roles separate. surveillance posses a very real threat to teh possibility of living complex, multi-layered social lives. This chapter considers question of how surveillance might affect the proper functioning of the rule of law, and the related question of how much surveillance is too much in a democratic society. The author considers: (1) the role of privacy in the protection of political rights; and (2) how much surveillance is too much?
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| 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".