(De)securitisation dilemmas: Theorising the simultaneous enaction of securitisation and desecuritisation
Bibliographic record
Abstract
Abstract This article theorises the simultaneous enaction of securitising and desecuritising moves. It argues that the frequent simultaneity of these two processes, which are normally considered mutually exclusive within Securitisation Theory (ST), has previously gone unnoticed given a set of methodological, temporal, and ontological biases that have developed within ST. Demonstrating how these biases can be overcome – and even reconciled with the seminal texts of ST – by drawing on work from within social theory and elsewhere, we argue that the frequent simultaneity of (de)securitising moves most urgently requires us to reconsider the normative status of desecuritisation within ST. Although desecuritisation has traditionally been viewed as normatively positive, we argue that its temporally immanent enaction alongside securitising moves might introduce more violence into security politics and, in fact, exacerbate protracted conflicts. Ultimately, we make the normative ambitions of some within ST more opaque. Desecuritisation is not a shortcut to the ethical-political good within ST.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.078 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".