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Record W2581038060 · doi:10.20361/g27w3x

Bubonic Panic: When Plague Invaded America by G. Jarrow

2017· article· en· W2581038060 on OpenAlexvenueaboutno aff
Tammy Flanders

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2017
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsPlague (disease)nobodyPoliticsPandemicHistoryChinaGeographyAncient historyEthnologyEconomic historyGenealogyDemographyDevelopment economicsPolitical scienceMedicineCoronavirus disease 2019 (COVID-19)DiseaseSociologyArchaeologyLawEconomics

Abstract

fetched live from OpenAlex

Jarrow, Gail. Bubonic Panic: When Plague Invaded America. Calkins Creek, 2015.If public health seems like it would be one of those topics that would send you to sleep, then Bubonic Plague: When Plague Invaded America by Gail Jarrow will change your mind. This is the final book in her trilogy about Deadly Diseases for middle grades and higher.Jarrow is fairly succinct in presenting the history, transmission, and trajectory of various waves of plague around the world. She briefly charts its first appearance in 541 in Turkey, then vividly describes the second wave that started in 1346 and at its most virulent was named the Black Death, killing millions in Europe and parts of Asia and North Africa. The majority of the book focuses on the third wave, when it reached North America.The third pandemic began in the mid-1800s when China became ground zero for this next wave, which spread to Hong Kong by 1894. Hong Kong was a busy port town and trade and travel on steamships allowed for rapid dispersion of the disease. Researchers from a number of countries sought feverishly to identify the source of the epidemic and learn how it was spread. By the late 1890s two of them had proven it was rat fleas. Unfortunately almost nobody believed them, which became problematic when in 1900 San Francisco saw its first deaths in Chinatown.Jarrow provides a fascinating look at the political and social climate of this period in relation to the attitudes of Americans towards Chinese immigrants and the impact quarantining San Francisco’s Chinatown would have on businesses reliant on trade and tourism. It became a complicated and fraught tug-o-war between politicians, businessmen, doctors and public health officials, fighting about whether to recognize and publicize the deaths and quarantine when the evidence seemed inconclusive as to their cause. Even after proof was offered action was surprisingly slow to follow and the disease was able to spread, although the number of deaths was comparatively low, being in the low hundreds.This well researched book also includes information about contemporary cases in the United States, ongoing research and treatments for all three strains of plague. There are extensive source notes and bibliography, a glossary, timeline, index and an author’s note explaining her keen interest in public health and the importance it had in the past,and will have when the next global pandemic hits. Also included are numerous photographs (some a little gruesome), newspaper clippings, cartoons, posters and illustrations to engage readers’ interest.This will pair perfectly with a middle grade novel, Chasing Secrets by Gennifer Choldenko, 2015 that gives a fictional account of the outbreak in San Francisco.This is a strong finish to a fascinating series that combines history, social issues, scientific research, technological developments and culture in America, showing long term implications for today’s government policies towards health.Highly Recommended: 4 out of 4 starsReviewer: Tammy FlandersTammy is the Reference Coordinator in the Doucette Library of Teaching Resources at the University of Calgary. She also reviews juvenile resources with an eye to classroom use in her blog, Apples with Many Seeds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0210.011

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.

Opus teacher head0.011
GPT teacher head0.296
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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