Surveillance as social sorting : privacy, risk, and digital discrimination
Bibliographic record
Abstract
Part One: Orientations 1. Surveillance as Social Sorting: Computer Codes and Mobile Bodies 2. Theorizing Surveillance: The Case of the Workplace 3. Biometrics and the Body as Information: Normative Issues of the Socio-technical Coding of the Body Part Two: Verifying Identities: Constituting Life-Chances 4. Electronic Identity Cards and Social Classification 5. Surveillance Creep in the Genetic Age 6. Racial Categories and Health Risks: Epidemiological Surveillance Among Canadian First Nations Part Three: Regulating Mobilities: Places and Spaces 7. Privacy and the Phenetic Urge: Geodemographics and the Changing Spatiality of Local Practice 8. People and Place: Patterns of Individual Identification within Intelligent Transportation Systems 9. Netscapes of Power: Convergence, Network Design, Walled Gardens, and other Strategies of Control in the Information Age Part Four: Targeting Trouble: Social Divisions 10. Categorizing the Workers: Electronic Surveillance and Social Ordering in the Call Centre 11. Private Security and Surveillance: From the Dossier Society to Database Networks 12. From Personal to Digital: CCTV, the Panopticon, and the Technological Meditation of Suspicion and Social Control
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".