New Worlds
Bibliographic record
Abstract
Abstract As if written by the moving finger in Fitzgerald’s famous free translation of Omar Khayyám’s Rubáiyá t, the story told in Chapter 4 of the emergence of epidemic poliomyelitis across Europe from the latter years of the nineteenth century was repeated again and again between 1900 and 1920, first in North America, then in Latin America, and finally in Oceania. It is this time ordered stepping stones model of the emergence of global epidemic poliomyelitis which we examine in this chapter. We begin in Section 5.2 with an overview of the evidence for the occurrence of poliomyelitis in the United States in the quarter century or so from 1881, a period which immediately preceded the onset of formal national level surveillance in that country. We then focus upon the first major outbreak in the United States, in New York in 1907 (Sect. 5.3), before studying in Section 5.4 the great epidemic of poliomyelitis which swept through New York City and the northeasttern United States in 1916. Next, we review in Section 5.5 a classic series of descriptions of poliomyelitis emergence in one state of New England: Charles S. Caverly’s records of poliomyelitis in Vermont, 1910–17. Finally, evidence for the appearance of poliomyelitis in other New World countries in the Americas and Oceania is summarized in Section 5.6. The chapter is concluded in Section 5.7.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.555 | 0.359 |
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".