Public health intelligence and the detection of potential pandemics
Bibliographic record
Abstract
This article considers contemporary developments in public health intelligence (PHI), especially their focus on health events of pandemic potential. It argues that the sociological study of PHI can yield important insights for the sociology of pandemics. PHI aims to detect health events as (or even before) they unfold. Whilst its apparatuses envelope traditional public health activities, such as epidemiological surveillance, they increasingly extend to non-traditional public health activities such as data-mining in electronically mediated social networks. With a focus on non-traditional PHI activities, the article first situates the study of PHI in relation to the sociology of public health. It then discusses the conceptualisation and actualisation of pandemics, reflecting on how public health professionals and organisations must equip themselves with diverse allies in order to realise the claims they make about pandemic phenomena. Finally, using the analytic tools of actor-network theory, sites for future empirical research that can contribute to the sociology of pandemics are suggested.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".