Soft Surveillance, Hard Consent
Bibliographic record
Abstract
This article explores how, like newer approaches to State paternalism, both public and private sector surveillance increasingly rely on what Gary Marx refers to as 'soft' measures. Taking their cue from the behavioral sciences, governments and businesses have come to realize that kinder, gentler approaches to personal information collection work just as well as coercion or deceit - and that engineering consent is the key to their success. In this article we contemplate various aspects of the role of consent in the collection, use and disclosure of personal information. After demonstrating how consent-gathering processes are often designed to quietly skew individual decision-making while preserving the illusion of free choice, we point out the dangers of these subtle schemes as well as the inadequacies of current privacy laws in dealing with them. In examining some potential remedies, we investigate the practical implications of data protection provisions that allow individuals to 'withdraw consent.' Canvassing recent interdisciplinary work in psychology and decision theory, we explain why such 'withdrawal of consent' provisions will not generally provide effective relief and argue that there is a need for a higher threshold of initial consent in privacy law than in private law.
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.
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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".