Bibliographic record
Abstract
The most fundamental issue this book has sought to address was the question of how pathogens interact with biophysical, political, economic, and cultural factors to produce and eventually control a contemporary urban disease outbreak.The sheer quantity of variables involved, and the complexity of the interactions amongst them (operating at and across different scaleslocal, regional, national, and global), necessitated the need to find a more encompassing approach.Correspondingly, the analytical challenge was to develop an appropriate perspective through which we could approach this seemingly unwieldy problematic in a manageable way.A clue to a potentially suitable entry point came from our initial observations that SARS spread through a network of economically and cultural significant citiesthat is, "global cities" that were linked together through the flows of people, information, capital, resources, technologies, and so on (Ali and Keil 2006).From here it was soon realized that perhaps the notion of networks itself could serve as central concept that would enable us to incorporate the myriad of factors implicated in the spread of SARS into our study, while at the same time allowing us to adequately capture the inherent complexity of an urban disease outbreak as an emergent phenomenon.In this light, one of the major objectives of this volume was to explicate the role of global networks (and networks more generally) in the contemporary spread of infectious disease.If networks were instrumental for the spread of disease, then important issues still remained for us as social scientists, specifically those concerning the political and socio-cultural ramifications of such networked spread.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.260 | 0.043 |
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".