Managing an epidemic in imperfect times: encampment and immunity passes in 19th century Gibraltar
Bibliographic record
Abstract
### Summary box As the COVID-19 pandemic surpasses a year for many populations around the world, there has been renewed interest in what the future holds for new and innovative non-pharmaceutical interventions as well as an interest in measures of the past and how they have evolved over time. While considerable attention has been placed on contrasts and parallels with the 1918/1919 influenza pandemic, one may ask are there any valuable lessons that we can learn about managing an epidemic when knowledge of the aetiology of diseases was imperfect? One such example of forward-thinking, centralised and proactive mitigation strategies dates back to the nineteenth century yellow fever epidemics in Gibraltar. As a peninsula with a limited territory of only 6.7 km2, bounded by three sides of sea and Spain to the north, Gibraltar was a British colony and garrison town under strict military governance and police surveillance. Gibraltar was uniquely …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".