Exploring Consciousness: The Online Community’s Understanding of Mobile Technology Surveillance
Bibliographic record
Abstract
This paper examines subjective understandings and experiences of mobile technology surveillance, an area of surveillance studies literature that is significantly underexplored in North America. Drawing on Ewick and Silbey’s (1998) popular socio-legal conception of legal consciousness, the paper constructs a similar concept in the domain of surveillance. Surveillance consciousness of two mobile surveillance technologies—drones and Stingrays—is explored through online data. Upon analyzing reactions to surveillance, the paper expounds on the complexities found therein, which conform to Ewick and Silbey’s tripartite set of schemas. Such complexities contribute to surveillance studies by addressing whether prevalent theoretical models of surveillance can be sufficiently used to capture the current surveillance society. In its entirety, this paper demonstrates how surveillance and socio-legal studies benefit from greater dialogue and cross-fertilization.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".