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Record W4399813371 · doi:10.1007/s10729-024-09674-7

Editorial: management science for pandemic prevention, preparedness, and response

2024· editorial· en· W4399813371 on OpenAlexaff
Hrayer Aprahamian, Vedat Verter, Manaf Zargoush

Bibliographic record

VenueHealth Care Management Science · 2024
Typeeditorial
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsMcMaster UniversityQueen's University
Fundersnot available
KeywordsHealth informaticsPandemicPreparednessHealth administrationCoronavirus disease 2019 (COVID-19)Nursing researchHealth care managementMedicinePublic health2019-20 coronavirus outbreakMedical emergencyPolitical scienceNursingVirologyPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.022
metaresearch head score (Gemma)0.091
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.033
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.091
Meta-epidemiology (narrow)0.0070.002
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0100.004
Science and technology studies0.0070.005
Scholarly communication0.0150.007
Open science0.0060.003
Research integrity0.0290.029
Insufficient payload (model declined to judge)0.0330.022

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 head0.102
GPT teacher head0.484
Teacher spread0.383 · 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
GenreEditorial

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

Citations2
Published2024
Admission routes1
Has abstractno

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