Sousveillance and Cyborglogs: A 30-Year Empirical Voyage through Ethical, Legal, and Policy Issues
Bibliographic record
Abstract
This paper describes the author's own personal experiences, experiments, and lifelong narrative of inventing, designing, building, and living with a variety of body-borne computer-based visual information capture and mediation devices. The emphasis is not just on the devices themselves, but on certain social, privacy, ethical, and legal questions and challenges that have arisen from actual experiences with lifelong video capture, processing, transmission, and dissemination in a variety of different everyday cultural settings over the past 30 years. The most interesting of these accidentally-found questions pertain to: (1) inverse surveillance (body-borne audiovisual and other sensor capture, storage, recall, and processing, known in the research literature as “sousveillance”); and (2) the epistemology of freewill and metaphysics of choice that seems to arise from an apparent reversal of the now pervasive and ubiquitous notion of surveillance. Extrapolating from these lessons, several hypotheses are presented, including: (1) sousveillance, like surveillance, will be driven by rapid development of new technology, leaving legal frameworks lagging behind technology; (2) the growth of sousveillance will accelerate greatly when implementations come with other non-sousveillance uses (e.g., camera phones because of their strategic ambiguity with regard to whether they are being used to take a picture or for just a voice call); (3) legal frameworks will tend to support rather than oppose sousveillance; (4) such legal protections will favor video sousveillance over video surveillance just as they now favor audio sousveillance over audio surveillance; (5) such legal protections will emerge first for the disabled (e.g., the visually impaired); and will then expand to encompass other legitimate and beneficial uses of sousveillance (personal safety, evidence gathering, etc.); (6) a person wishing to do lifelong sousveillance is deserving of certain legal protections liabilizing others who might attempt to disrupt continuity of evidence.
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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 | 0.037 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".