Power in the modern ‘surveillance society’: From theory to methodology <sup/>
Bibliographic record
Abstract
The rapid expansion of new Information and Communication Technologies has improved the possibilities for surveillance, rendering modern society a ‘surveillance society’ (Lyon, 2006). Surveillance practices today comprise a myriad of actors. However, relations between different groups of “observers” and “observed” and their respective impact on the form of surveillance are not yet sufficiently considered. Furthermore, methodologies are missing “to look beyond abstract theory” (Galič et al., 2017, p. 34). This paper proposes theoretical considerations as well as a methodological framework by taking a meso-level perspective and by incorporating the examination of power relations in surveillance systems. It is argued that contemporary surveillance structures encompass hierarchies, albeit not in a traditional unidirectional manner. Furthermore, a first attempt is made to provide a methodological framework that helps to analyse the power relationships between diverse actors that emerge due to differences in capabilities to observe and hide. Based on a number of specified indicators, the framework aims to assist in understanding how power is distributed and in how far actors and their position within the hierarchy determine the form of surveillance and the impact it can take.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.054 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".