‘Framing’ the Copenhagen School: Integrating the Literature on Threat Construction
Bibliographic record
Abstract
In recent years, the field of security studies has turned increasing attention to the processes through which security threats are constructed. Two approaches in particular have flourished, securitisation and framing, but scholars working within these frameworks generally do not draw on the other body of literature to inform theoretical development or to accumulate cases. This has resulted in the production of parallel literatures and the duplication of concepts and terminology, and has hindered the development of theory. This article demonstrates that securitisation and framing are substantively similar research programmes, that security operates as one of a number of distinct master frames, and that securitisation should be viewed as a subfield of framing. This would produce a division of labour whereby securitisation scholars could continue to focus on the distinct realm of security, while drawing on the broader framing literature to inform a number of areas that are under-theorised in present securitisation studies, such as audience acceptance, non-linguistic communicative forms, empowerment and marginalisation, and resistance and desecuritisation.
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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| 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".