Bringing Publics into Critical Security Studies: Notes for a Research Strategy
Bibliographic record
Abstract
Publics are an undertheorised and somewhat marginal presence in critical security studies. This article argues that a better understanding of publics can advance our understanding of the governance as well as the contestation of security regimes and practices. We develop this argument in three parts. First, we discuss the marginality of publics in critical security studies while highlighting those limited instances where publics have been engaged. Second, we direct attention to emerging research on publics in cognate disciplines, focusing in particular on the literature about material publics. We distil from this work some useful lessons for security studies. In a final section we suggest two research moves for promoting a stronger focus on publics within critical security studies. We conclude that a focus on material publics can furnish security studies with a better understanding of the phenomenon of politics.
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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 | 0.105 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.016 | 0.105 |
| Scholarly communication | 0.026 | 0.090 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.015 | 0.028 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".