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 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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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