Regulating security policy and practice via a norm of just securitization
Bibliographic record
Abstract
It is not uncommon for security practitioners to think that ethics is for secure times only. In times of emergency, the thinking goes, there simply isn’t time to check possible responses against ethical criteria. However, the well-documented unethical excesses of the war on terrorism (domestically and internationally) have instilled a wariness in politicians’ and security practitioners’ conduct and morals that increasingly compel security practitioners not to sideline ethics. The same is aided by our rampant social media culture that, for all its faults, offers unprecedented levels of scrutiny of government policy and conduct. In short, there has never been a better time for pushing a norm of just securitization, one that seeks to regulate the just initiation of securitization, just conduct of securitization, and just termination of securitization. This chapter explains what an ideal of such a norm would look like. But how practicable are these principles in the real world? In order to establish this, the chapter examines three real-world cases: securitization against infectious disease in Canada, securitization against terrorism in the EU and securitization against drought in Cape Town, South Africa, for evidence of the principles of just securitization. <br/><br/>
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".