Microbes, Mad Cows and Militaries: Exploring the Links Between Health and Security
Bibliographic record
Abstract
Abstract The 'securitization' of health has generated considerable debate. In public health, the debate focuses mainly on health effects. Although securitization may refocus attention and resources toward certain health issues, it may focus undue attention on a few issues or on the military aspects of issues to the detriment of a broad range of health issues and their human rights aspects. In international relations, the concern is the effect on security analysis and policy. While some welcome a broadening of the security agenda to include items such as health, others are concerned that analytical rigour and operational effectiveness are lost. This article argues that, normative concerns notwithstanding, securitizing is occurring as a result of perceived changes, associated with globalization, that are creating changes in the nature or degree of threats. But, in international relations, security is largely a social construction, as the Copenhagen School claims. Contemporary social struggles are ongoing around competitions to define security. The article argues that human security is a concept that has considerable relevance for understanding the nature of change that is producing new or intensified threats. It also offers conceptual space for analyzing what security is provided and for whom in the changing world order.
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 | 0.002 | 0.003 |
| 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 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".