Questioning security devices: Performativity, resistance, politics
Bibliographic record
Abstract
Abstract Algorithms, biometrics and body scanners, computers and databases, infrastructures of various kinds, ranging from what is commonly referred to as ‘hi-tech’ to ‘low-tech’ items such as walls or paper files, have garnered increased attention in critical approaches to (in)security. This article introduces a special issue whose contributions aim to further these approaches by questioning the role and political effects of security devices. It proposes an analytics of devices to examine the configuration and reconfiguration of security practices by attending to the equipment or instrumentation that make these practices possible and temporally stabilize them. The aim here is not to advance devices as a new unit of analysis, but to open new forays in ongoing debates about security politics and practices, by asking different research questions and developing new research angles. We start by outlining what is at stake when thinking of and analysing security practices through devices, or shifting from the language of technology and ‘technologies of security’ to security devices. The remainder of the article then specifies how an analytics of devices involves a more varied vocabulary of performativity, on the one hand, and agency, on the other.
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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.155 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".