Bibliographic record
Abstract
Abstract The outbreaks of severe acute respiratory syndrome (SARS) that unfolded at various locations throughout the world represented the first collective threat to public health that was amplified by the processes and structures of our contemporary globalized society – such as, the compression of time and space and increased linkages between various cities of the world. In this article, the global outbreak of SARS in 2003 is used as an empirical referent to discuss the implications of infectious disease spread among and within cities under the conditions of globalization. To capture the uniquely dynamic qualities associated with infectious disease outbreaks under globalizing conditions, we suggest that conventional accounts of the spatial diffusion of pathogens incorporate topological principles that are sensitive to such properties as: fluidity, flows, mobility and networks, that now play a critical role in disease diffusion. If the rise of farming was . . . a bonanza for our microbes, the rise of cities was a greater one, as still more densely packed human populations festered under even worse sanitation conditions. (, 205) The explosive increase of world travel by Americans, and in immigration to the United States, is turning us into another melting pot – this time, of microbes that we previously dismissed as just causing exotic diseases in far‐off countries. (, 206)
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.001 |
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".