New Media and the power politics of sousveillance in a surveillance-dominated world.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it