Bibliographic record
Abstract
The aim of this paper is to identify a construct which may be used to frame the subjective experience of surveillance in contemporary society. The paper's central question concerns whether there is a concept to describe the experience of surveillance which can then inform empirical studies. Surveillance practice has consequences for the individual, yet surveillance studies do not have a particular take on the subject. Building on some preliminary empirical observations from the workplace, the paper suggests that the notion of ‘exposure’ is a useful starting point. The paper explores the range of ways in which subjects can be exposed under surveillance, and theoretically locates the concept in relation to developments in organization theory, new media theory and surveillance theory. Two observations are made which support the centrality of the ‘exposure’ concept within studies of surveillance. The first argument is that the body interior of the surveilled subject is more open to division, classification and scrutiny, because it is seen as a source of truth, the target of public revelation or fetish. There is now a political economy around the revelation of this interiority, which calls for a non-reductive and multi-dimensional approach to the subjective experience of surveillance. The second argument is that the nature and character of exposure are products of institutional configurations, which have consequences at the level of the individual. A research agenda is developed which will frame future work exploring the experience of surveillance.
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.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
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".