Tracking devices: On the reception of a novel security good
Bibliographic record
Abstract
In this article, we describe and make sense of the reception of a novel security good: namely, the personal GPS tracking device. There is nothing new about tracking. Electronic monitoring is an established technology with many taken-for-granted uses. Against this backdrop, we focus on a particular juncture in the ‘social life’ of tracking, the moment at which personal trackers were novel goods in the early stages of being brought to market and promoted as protective devices. Using data generated in a wider study of security consumption, our concern is to understand how this extension of tracking technology into everyday routines and social relations was received by its intended consumers and users. How do potential buyers or users respond to these novel protective devices? What is seductive or repulsive about keeping track of those towards whom one has a duty or relationship of care? How do new tracking technologies intersect with – challenge, reshape or get pushed back by – existing social practices and norms, most obviously around questions of risk, responsibility, trust, autonomy and privacy? This article sets out to answer these questions and to consider what the reception of this novel commodity can tell us about the meaning and future of security.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".