What kind of theory – if any – is securitization?
Bibliographic record
Abstract
One of the great appeals of securitization theory, and a major reason for its success, has been its usefulness as a tool for empirical research: an analytic framework capable of practical application. However, the development of securitization has raised several criticisms, the most important of which concern the nature of securitization theory. In fact, the appropriate methods, the research puzzles and type of evidence accepted all derive to a great extent from the kind of theory scholars bequeath their faith to. This Forum addresses the following questions: What type of theory (if any) is securitization? How many kinds of theories of securitization do we have? How can the differences between theories of securitization be drawn? What is the status of exceptionalism within securitization theories, and what difference does it make to their understandings of the relationship between security and politics? Finally, if securitization commands that leaders act now before it is too late, what status has temporality therein? Is temporality enabling securitization to absorb risk analysis or does it expose its inherent theoretical limits?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.051 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".