Agents without agency: Assessing the role of the audience in securitization theory
Bibliographic record
Abstract
Abstract This article assesses the role of the audience in securitization theory. The main argument is that in order to accurately capture the role of the securitization audience, it must be theorized as an active agent, capable of having a meaningful effect on the intersubjective construction of security values. Through a meta-synthesis of 32 empirical studies of securitization, this article focuses on two central questions: (1) Who is the audience? (2) How does the audience engage in the construction of security? When assessed against the theoretical works on securitization, this analysis reveals that the manner in which the audience is defined and characterized within securitization theory differs with the empirical literature that investigates securitization processes. Where the empirical literature suggests securitization is a highly intersubjective process involving active audiences, securitization theory characterizes audiences as agents without agency, thereby marginalizing the theory’s intersubjective nature. This article sketches a new characterization of the securitization audience and outlines a framework for securitizing actor–audience interaction that better accounts for securitization theory’s linguistic and intersubjective character, addresses this theoretical/empirical conflict, and improves our understanding of how groups select and justify security priorities and costly security policies.
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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 | 0.054 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".