Tracing and explaining securitization: Social mechanisms, process tracing and the securitization of irregular migration
Bibliographic record
Abstract
Abstract This article offers a process-mechanism explanation of securitization. To make the case for a process-mechanism account more concrete, I use interpretivist process tracing to explain the crisis episode of the Sun Sea, a Thai cargo ship carrying Sri Lankan asylum-seekers, and the securitization of irregular migration in Canada. Drawing on interviews and grey literature, the article shows how securitization was possible and under what conditions, and argues that ideational dispositions of security organizations induced state officials toward a security interpretation of the the Sun Sea. The article aims to demonstrate that process-mechanism explanations represent a compelling methodological alternative with which to trace and explain securitization. The article sees itself as part of a broader refinement of a sociological variant of securitization theory. It seeks to examine and enhance the contribution that this ‘post-Copenhagen’ approach – its core assumptions and theoretical framework – makes to the analysis of 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.046 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".