Bibliographic record
Abstract
Myriam Dunn Cavelty's new book, Cyber-Security and Threat Politics: US Efforts to Secure the Information Age, provides a theoretically informed analysis of the social construction of cyber-security threats in the context of US national security policy. As Cavelty notes, the topic of cyber-security has gone through many ebbs and flows over the years. During the 1990s, prior to 9/11, the concept was ranked extraordinarily high, with haunting prognostications of an electronic Pearl Harbor. Similar fears arose leading up to the year 2000, with the so-called Y2K crisis. Both threats reflected the growing recognition of our dependence on technological systems and the possibility of systems crash. After 9/11, cyber-security fears receded relative to more physical ones, like biological terrorism, although the issue has continued to morph and evolve. Cavelty's starting point is the so-called Copenhagen School of securitization (Buzan, Waever, and de Wilde 1997), which is within the social constructivist family of IR theorizing. According to this school, threats to national security are not defined in accordance with rational calculations, but are socially constructed from discourses that arise from and are shaped and promoted by policy communities. Socially constructed threats define the “object” of security (that which is to be protected), the agency or source of the threat, and the policy responses that flow from it, none of which is a priori self-evident. Cavelty adds some helpful conceptual elements to the Copenhagen School, such as threat frames and policy windows, which provide some additional theoretical depth, before embarking on her analysis of the US cyber-security threat frame. The theoretical parts of the book are very clearly written, easy to understand, and refreshingly self-conscious. It is clear that Cavelty aims not to proselytize her approach but rather assess it pragmatically as a tool. For that reason alone, the book is a useful primer on securitization theory (although because of its empirical focus on cyber-security it is likely not to be read as a general interest theoretical book).
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.072 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".