The Social and Political Context of Disease Outbreaks: The Case of SARS in Toronto
Bibliographic record
Abstract
Dans cette étude, nous avons utilisé un cadre théorique relevant de l'écologie politique afin de construire un modèle critique et systémique pour expliquer comment il est possible de gérer une maladie infectieuse émergente, comme le SRAS, dans notre univers mondialisé. Nous espérons qu'un tel modèle contribuera à la mise en place de politiques de gestion des risques plus réalistes. Nous commençons par établir et analyser les interactions qui, dans l'environnement social et humain, ont facilité l'apparition de l'épidémie de SRAS dans un contexte local, celui de Toronto. Ensuite, nous montrons que cette épidémie a mis en lumière les insuffisances profondément ancrées du système actuel de gestion mondiale de la santé. Nous mettons l'accent sur le fait que, en cette ère de mondialisation, il est imprudent de trop concentrer efforts sur le plan local. L'analyse des épidémies doit plutôt se faire dans une perspective mondiale, et doit tenir compte du fait que les liens entre les pays développés et les pays en développement relèvent de l'écologie politique. We adopt a political ecology framework to delineate a critical and systemic model that explains how an emerging infectious disease (EID), such as SARS, is dealt with in our globalized world. It is our hope that such a model will contribute to the development of more realistic risk-management policies. First, we focus on identifying and analyzing particular social and human-environment interactions that facilitated the spread of SARS within a local Toronto context. Second, we describe how the SARS outbreak brought to light the deeply rooted inadequacies involved in the current system of global health governance. We stress that in our globalized world it is unwise to focus too narrowly on the local context. The analysis of disease outbreaks must adopt a global perspective that considers the political ecological nature of the relationships between the developed and developing world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".