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Record W3134421977 · doi:10.3138/cart-2020-0025

Schools as Vectors of Infectious Disease Transmission during the 1918 Influenza Pandemic

2021· article· fr· W3134421977 on OpenAlexaffvenue
Don Lafreniere, Timothy J. Stone, Rose Hildebrandt, Richard C. Sadler, Michael J. Madison, Dan Trepal, Gary Spikberg, James Juip

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2021
Typearticle
Languagefr
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsWestern University
Fundersnot available
KeywordsHumanitiesInfluenza pandemicPolitical scienceCoronavirus disease 2019 (COVID-19)PhilosophyMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Les auteurs utilisent une combinaison de microdonnées nationales tirées des séries IPUMS ( Integrated Public Use Microdata Series) et de microdonnées sur la population et la santé au niveau régional, spatialisées à l’échelon des ménages, et ils se servent d’un SIG historique (SIGH) pour suivre la transmission de l’infection grippale entre les enfants des écoles publiques de la péninsule nord du Michigan durant la pandémie de 1918. Les microdonnées sont des données non agrégées d’un extrême degré de précision. Les auteurs décrivent trois avantages importants de l’utilisation de microdonnées historiques dans le contexte du SIGH : la contextualisation des données dans l’espace et le temps en correspondance avec la période, l’esquive de l’erreur écologique et la capacité de naviguer librement entre les échelles micro et macro. Ils montrent le potentiel qu’offre l’étude historique des pandémies au moyen de microdonnées historiques en procédant à une analyse spatiotemporelle de cette maladie respiratoire infectieuse dans trois écoles, d’avril à juin 1918.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.306
Teacher spread0.295 · 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 teacher head, 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

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicData-Driven Disease SurveillanceFrench-language works237,207