Everyday Life in the Culture of Surveillance
Bibliographic record
Abstract
Over the recent decades, the possibilities to surveil people have increased and been refined with the ongoing digital transformation of society. Surveillance can now go in any direction, and various forms of online surveillance saturate most people’s lives, which are increasingly lived in digital environments. To understand this situation and nuance the contemporary discussions about surveillance – not least in the highly digitalised context of the Nordic countries – we must adopt cultural and ethical perspectives in studying people’s attitudes, motives, and behaviours. The “culture of surveillance”, to borrow David Lyon’s term, is a culture where questions about privacy and publicness, and rights and benefits, are once again brought to the fore. This anthology takes up this challenge, with contributions from a variety of disciplinary and theoretical frameworks that discuss and shed light on the complexity of contemporary surveillance and thus problematise power relations between the many actors involved in the development and performance of surveillance culture. The contributions highlight how more and more actors and practices play a part in our increasingly digitalised society. The book is an outcome of the research project "iAccept: Soft surveillance – between acceptance and resistance", financed by the Marcus and Amalia Wallenberg Foundation. The anthology’s editors are project members, all based at Umeå University, Sweden: Lars Samuelsson, associate professor of philosophy; Coppélie Cocq, professor of Sámi studies and digital humanities; Stefan Gelfgren, associate professor of sociology of religion; and Jesper Enbom, associate professor of media studies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.053 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".