Bibliographic record
Abstract
List of Figures. List of Tables. Notes on Contributors. Series Editors' Preface. Preface. Introduction: Networked Disease ( S. Harris Ali and Roger Keil ). Part I: Infectious Disease and Globalized Urbanization. Introduction ( S. Harris Ali and Roger Keil ). 1 Toward a Dialectical Understanding of Networked Disease in the Global City: Vulnerability, Connectivity, Topologies ( Estair Van Wagner ). 2 and Disease in Global Cities: A Neglected Dimension of National Policy ( Victor G. Rodwin ). Part II: SARS and Governance in the Global City: Toronto, Hong Kong, and Singapore. Introduction ( S. Harris Ali and Roger Keil ). 3 SARS and the Restructuring of Governance in Toronto ( Roger Keil and S. Harris Ali ). 4 Globalization of SARS and Governance in Hong Kong under One Country, Two Systems ( Mee Kam Ng ). 5 Surveillance in a Globalizing City: Singapore's Battle against SARS ( Peggy Teo, Brenda S.A. Yeoh, and Shir Nee Ong ). Part III: The Cultural Construction of Disease in the Global City. Introduction ( S. Harris Ali and Roger Keil ). 6 The Troubled Public Sphere and Media Coverage of the 2003 Toronto SARS Outbreak ( Daniel Drache and David Clifton ). 7 SARS as a Health Scare ( Claire Hooker ). 8 City under Siege: Authoritarian Toleration, Mask Culture, and the SARS Crisis in Hong Kong ( Peter Baehr ). 9 Racism is a Weapon of Mass Destruction: SARS and the Social Fabric of Urban Multiculturalism ( Roger Keil and S. Harris Ali ). Part IV: Re-Emerging Infectious Disease, Urban Public Health, and Global Biosecurity. Introduction ( S. Harris Ali and Roger Keil ). 10 Deadly Alliances: Death, Disease, and the Global Politics of Public ( Matthew Gandy ). 11 Tuberculosis and the Anxieties of Containment ( Susan Craddock ). 12 Networks, Disease, and the Utopian Impulse ( Nicholas B. King ). 13 People, Animals, and Biosecurity in and through Cities ( Steve Hinchliffe and Nick Bingham ). Part V: Networked Disease: Theoretical Approaches. Introduction ( S. Harris Ali and Roger Keil ). 14 SARS as an Emergent Complex: Toward a Networked Approach to Urban Infectious Disease ( S. Harris Ali ). 15 Thinking the City through SARS: Bodies, Topologies, Politics ( Bruce Braun ). 16 Vapors, Viruses, Resistance(s): The Trace of Infection in the Work of Michel Foucault ( Philipp Sarasin ). 17 Fleshy Traffic, Feverish Borders: Blood, Birds, and Civet Cats in Cities Brimming with Intimate Commodities ( Paul Jackson ). Concluding Remarks ( Roger Keil and S. Harris Ali ). Bibliography. Index.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.490 | 0.232 |
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".