Bibliographic record
Abstract
Tom Koch’s book is a work of art that lovingly brings together hundreds of contemporary computer-generated and historical line-drawn maps to tell a tale of disease. Beginning by outlining the parallels between the developments of analytical cartography of the city and scientific anatomy of the body, Disease Maps is an attempt to link Geography and Medicine, playing up the importance of images: ‘For centuries the map has been a mechanism by which the rolls of the dead and the dying became shared realities whose relation to local environmental conditions could be assessed’ (p. 2). The author himself says that his central argument is that we need to think about visualization, about ‘seeing’ at every scale (p. 4).To do this he tells numerous stories with maps and stories about maps. Most of these stories have a common format. They concern the cartographic search for the source of a particular disease. Early on in the book the disease is an outbreak of Salmonella enteritidus in British Columbia in the year 2000 and the source is traced back to a cream custard: In the Vancouver example the patients were blameless and responsibility assignable in part to the local bakery whose cream custards were the apparent source of the outbreak. But the local baker brought supplies from wholesalers and they carried a predicate responsibility. Ultimately, the final responsibility rested with the hospitals that treated the patients and the health agencies that in theory but not always in practice assure restaurants and food producer practices are safe. (p. 29) I have no quibbles with the arguments against blaming the victims, or against seeing us all, through the agencies we fund and the hospitals we support, as being responsible, I have great admiration for the amount of work that has gone into documenting the stories behind so many maps and outbreaks in this book, I find Koch’s arguments that there are no real heroes of great use, but I have a concern.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".