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Record W3035585525 · doi:10.33137/utjph.v1i1.34435

Four Dates, One Future

2020· article· en· W3035585525 on OpenAlexafffundabout
Daniel A. Harris, Jean-Paul R. Soucy, David J. Kinitz, Kuan Liu, Aravind Rajendran, Shelby L. Sturrock, Kate St. Cyr, Rebecca Christensen

Bibliographic record

VenueUniversity of Toronto Journal of Public Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersUniversity of Toronto
KeywordsPublic healthScholarshipInternational healthContext (archaeology)HonourBiostatisticsHealth promotionPublic relationsSanitationPopulation healthHealth policyPolitical scienceSocial scienceSociologyMedicineGeographyNursing

Abstract

fetched live from OpenAlex

For nearly 150 years the University of Toronto has integrated public health into its teaching and research. From early lectures in sanitation (1871) to the discovery of insulin (1921), the University of Toronto’s rich history is reflected in its prominence as a global leader in public health research and education. Therefore, it is fitting for the University of Toronto to host an academic journal of public health that showcases both high-impact scholarship and public health practice. Founded in 2020, the University of Toronto Journal of Public Health has an ambitious, yet essential, vision: to foster the next generation of public health researchers and practitioners in order to improve population health nationally and globally. In this editorial, we honour the diverse and complementary nature of the fields of biostatistics, epidemiology, health policy and practice, and social and behavioural health sciences by highlighting an important historical date from each. We reflect on these milestones within a historical and contemporary context, and conclude by considering the importance of each discipline for the future of public health in Canada and abroad.

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)0.018
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 score0.112
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0190.024
Scholarly communication0.0260.023
Open science0.0030.012
Research integrity0.0060.016
Insufficient payload (model declined to judge)0.0440.012

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 head0.167
GPT teacher head0.393
Teacher spread0.226 · 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

Citations0
Published2020
Admission routes3
Has abstractyes

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