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Record W1476810609 · doi:10.1177/00333549101250s315

Immigration, Ethnicity, and the Pandemic

2010· article· en· W1476810609 on OpenAlexaboutno aff
Alan M. Kraut

Bibliographic record

VenuePublic Health Reports · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Health, Geopolitics, Historical Geography
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationEthnic groupPandemicPublic healthNewspaperPrejudice (legal term)Political scienceInfluenza pandemicGeographyDemographyEconomic growthMedicineInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)SociologyDiseaseLaw

Abstract

fetched live from OpenAlex

The influenza pandemic of 1918-1919 coincided with a major wave of immigration to the United States. More than 23.5 million newcomers arrived between 1880 and the 1920s, mostly from Southern and Eastern Europe, Asia, Canada, and Mexico. During earlier epidemics, the foreign-born were often stigmatized as disease carriers whose very presence endangered their hosts. Because this influenza struck individuals of all groups and classes throughout the country, no single immigrant group was blamed, although there were many local cases of medicalized prejudice. The foreign-born needed information and assistance in coping with influenza. Among the two largest immigrant groups, Southern Italians and Eastern European Jews, immigrant physicians, community spokespeople, newspapers, and religious and fraternal groups shouldered the burden. They disseminated public health information to their respective communities in culturally sensitive manners and in the languages the newcomers understood, offering crucial services to immigrants and American public health officials.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.038
GPT teacher head0.346
Teacher spread0.309 · 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
GenreOther

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

Citations76
Published2010
Admission routes1
Has abstractyes

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Same venuePublic Health ReportsSame topicMigration, Health, Geopolitics, Historical GeographyFrench-language works237,207