Two decades under the influence of urbanization and infectious disease
Bibliographic record
Abstract
This article outlines the major aspects of Roger Keil's contributions to the study of the relationship between cities and infectious disease to date. We suggest how urban scholars have already and could in the future build on his corpus of work, not just for urban health specifically, but for our grasp of extended urbanization more generally. We do so by recounting Keil's contribution to the urban studies scholarship on various dimensions, including those pertaining to: the networking of urban health, managing urban health within the context of extended urbanization, and finally theoretically grounding urban health into a finer-grained critical grasp of urban questions in our post-anthropocentric era. In our conclusion, we highlight the urgent and continuing need to embrace Roger Keil's steadfast optimism even when facing deeply troubling urban crises—a lesson all too critical today as global affairs veer toward more turbulent and calamitous times, such as those faced in the current geopolitical situation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".