New Media and the power politics of sousveillance in a surveillance-dominated world.
Bibliographic record
Abstract
In this paper we address the increasingly complex constructs between power, and the practices of looking, in a mediated, mobile and networked culture. We develop and explore a nuanced understanding and ontology that examines veillance in both directions: surveillance and oversight, as well as sousveillance and “undersight”. In particular, we unpack the new relationships of power and democracy facilitated by mobile and pervasive computing. We differentiate between the power relationships in the generalized practices of looking or gazing, which we place under the broad term “veillance”. Then we address the more subtle distinctions between different forms of veillance that we classify as surveillance and sousveillance, as well as McVeillance (the ratio of surveillance to sousveillance). We start by unpacking this understanding to develop a more specialized vocabulary to talk not just about oversight but also to talk about the implications of mobile technologies on “who watches the watchers”. We argue that the time for sousveillance, as a social tool for political action, is reaching a critical mass facilitated by a convergence of transmission, mobility and media channels for content distribution and engagement. Mobile ubiquitous computing, image capture and processing, and seamless connectivity of every iPad, iPhone, Android Device, wearable computer, etc., allows for unprecedented ‘on the ground’ watching of everyday life. The critical mass of ‘sousveillant’ capable devices in everyday life may make the practice of sousveillance a potentially effective political force that. Sousveillance can now challenge and balances the possibility for corruption that is inherent in a surveillance-only society (i.e. one that has only oversight without undersight).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".