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Record W4401811915 · doi:10.55016/ojs/sppp.v13i1.70310

Public Policy Trends: Unequal Burden: Learning from Canada's Responses to the Influenza Pandemic of 1918-20

2020· article· en· W4401811915 on OpenAlexaffabout
Shawn Brackett

Bibliographic record

VenueThe School of Public Policy Publications · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPandemicInfluenza pandemicCoronavirus disease 2019 (COVID-19)Public healthPandemic influenza2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political scienceEconomic growthVirologyMedicineEconomicsInfectious disease (medical specialty)DiseaseNursingOutbreak

Abstract

fetched live from OpenAlex

Analyzing Canada's responses to the 1918-20 influenza pandemic can offer insights into the current policy and social context of the coronavirus pandemic, while also helping to ensure we do not repeat past mistakes.In spring 1918, a novel influenza frequently called "Spanish Flu" arrived in Canada.Most authorities took little action because of its shared symptoms with seasonal influenza and its low mortality.However, six months later the influenza had mutated and its second-and most deadly-wave crashed down.Landing first in the Maritimes, Quebec, and Ontario via American travelers from New England and Canadian soldiers returning from Europe, influenza rapidly spread westward with railroad traffic to the Prairies and British Columbia.Of the estimated 50 million global deaths as a result of influenza, about 55 000 were Canadian (Fahrni & Jones 2012, 4).

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)0.008
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.167
Threshold uncertainty score0.966

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0340.012
Scholarly communication0.0180.012
Open science0.0030.010
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0140.001

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 head0.212
GPT teacher head0.455
Teacher spread0.243 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2020
Admission routes2
Has abstractyes

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