Surveillance capitalism and systemic digital risk: The imperative to collect and connect and the risks of interconnectedness
Bibliographic record
Abstract
Zuboff's The Age of Surveillance Capitalism provides a powerful analysis of the emergence of surveillance capitalism as a particular type of informational capitalism. Many of the important impacts of this project of creating larger and more integrated systems of ‘behavioural surplus’ are captured powerfully by Zuboff; yet as different risk and organisational scholars such as Beck, Perrow, and Vaughan have argued, integrated systems often do not function as intended. While the imperfection of these systems may raise the possibility that surveillance capitalism may not be as bad as Zuboff suggests, there is also a way in which these systems not functioning as intended can make surveillance capitalism an even more dystopian possibility. In this vein, this paper asks: what are the consequences when the tools of a surveillance capitalist society break down? This paper argues that it is by thinking through Zuboff's framework that we can identify the systemic fragility of a surveillance capitalist society. This systemic fragility emerges through how surveillance capitalism generates imperatives towards the maximal collection of data for exploitation, which in turn generates a corresponding imperative to connect all aspects of life. Both of these imperatives, of collect and connect, in turn create an immensely fragile digital system, which has vast ramifications throughout social life, such that small imperfections and gaps in the system can magnify risk throughout society.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".