On Assemblages and Surveillantization: Thinking and Rethinking Surveillance Theory
Bibliographic record
Abstract
It has been over a quarter of a century since I wrote The Surveillant Assemblage with my colleague Richard Ericson. In it, we advance a theoretical model for understanding key aspects of surveillance, aiming to move beyond portraying it as a form of totalitarian “Big Brother” or Foucauldian panopticism. Inspired by Gilles Deleuze and others, we detailed how surveillance operates by integrating heterogeneous practices and technologies, contributing to the production, combination, and movement of data flows. Among other dynamics, we accentuated fluidity and processes of emergence, integration, and the proliferation of data doubles. Scholars in fields as different as media studies, criminology, anthropology, critical race studies, data science, gender studies, architecture, law, and sociology have applied the model or debated its relevance to a wide range of empirical settings. But no model is fully comprehensive, and developments in surveillance now move rapidly. As such, it has been invigorating to revisit this article and rethink how to adequately theorize surveillance. My comments here set out to do three things. First, I use this opportunity to briefly introduce the notion of “surveillantization,” which I see as a valuable way to conceive of some of the “big picture” developments and trajectories in surveillance. Second, I return to the surveillant assemblage to offer a few thoughts on the commentators’ essays in this forum. The concluding section is a coda that provides background on writing the original surveillant assemblage article. It offers a glimpse into one small part of the early history of surveillance studies.
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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.073 |
| Scholarly communication | 0.015 | 0.050 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".