Bibliographic record
Abstract
In 2015, University of Wisconsin historian Richard Keller published a book called Fatal Isolation: The Devastating Paris Heatwave of 2003. He happened to be in Paris that summer and promptly started collecting data on the health impacts and death toll of the record-breaking temperatures. Despite the book taking about a decade to write, when it was published, Keller debated with his colleagues about whether it was really a history book, since, in his critic’s words, ‘This just happened’. Jacalyn Duffin lays claim to the first COVID-19 history with her new book, which may prompt historians to ask, can we have a history of something that we are practically still experiencing? We suspect her answer would echo those of other historians who write about recent events: every event can be placed into historical context and its consequences can be historicised. In this regard, COVID, while a ‘novel’ coronavirus, is nonetheless an infectious disease like others throughout history, with a context that influenced how we managed it and an impact on society that can be historicised. Thus, this book joins a vast library of infectious disease histories with lessons for the present, including classics by her mentor, Mirko Grmek. In continuity with this literature, Duffin highlights our tendency to scapegoat and blame racialised people during disease outbreaks.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.203 | 0.083 |
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".