Securitization theory and its empirical application: a literature review
Bibliographic record
Abstract
ABSTRACT Introduction: Securitization theory posits that securitization happens when actors frame political agenda issues as existential threats through their discourse, prompting states to take action in response. This article explores the challenges in the empirical application of the Copenhagen School's securitization theory in International Relations research. Materials and methods: We conducted a systematic review of articles published in journals indexed in the Scopus database with an impact factor in the first quartile. Initially, we selected 260 articles that mentioned the term “securitization/securitisation” in their titles, abstracts, or keywords. After excluding those lacking an empirical application of securitization theory, 184 articles remained. We then carried out a content analysis of the logical structure of these articles' arguments, categorizing how each one applied the concept of securitization according to the stages of the process (non-politicized, politicized, securitized, securitizing actor) and its variables. Results: Out of the 184 articles, 110 set out to apply securitization theory, but only 11 successfully did so in a way that clearly confirmed securitization. These 11 studies showed how topics were securitized by following the stages outlined in the original theoretical framework. Discussion: The challenges in empirically applying securitization theory arise from two main factors: the researchers themselves and the theory itself. Many articles faced methodological hurdles and lacked rigor in operationalizing the theoretical elements required to confirm the securitization of a topic, revealing limitations among the researchers. Additionally, the theory demands a high level of empirical evidence, which makes its application more difficult. This indicates a need to revisit the theory and consider integrating models that facilitate empirical studies on securitization.
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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 | 0.020 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.028 | 0.037 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".