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Record W4248355664 · doi:10.21203/rs.3.rs-177881/v1

Learning from Public Health and Hospital Resilience to the SARS-CoV-2 Pandemic: Protocol for a Multiple Case Study (Brazil, Canada, China, France, Japan, and Mali).

2021· preprint· en· W4248355664 on OpenAlexaffabout
Valéry Ridde, Lara Gautier, Christian Dagenais, Fanny Chabrol, Renyou Hou, Pierre‐Marie David, Patrick Cloos, Arnaud Duhoux, Jean‐Christophe Lucet, Lola Traverson, Sydia Rosana de Araújo Oliveira, Gisèle Cazarin, Nathan Peiffer‐Smadja, Laurence Touré, Abdourahmane Coulibaly, Ayako Honda, Shinichiro Noda, Toyomitsu Tamura, Hiroko Baba, Haruka Kodoi, Kate Zinszer

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversité de Montréal
FundersAgence Nationale de la Recherche
KeywordsPandemicChinaCoronavirus disease 2019 (COVID-19)Resilience (materials science)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public healthProtocol (science)2019-20 coronavirus outbreakPolitical scienceEconomic growthGeographyMedicineVirologyNursingAlternative medicineEconomicsInfectious disease (medical specialty)OutbreakDisease

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)0.051
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.974
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.055
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0090.004
Scholarly communication0.0030.003
Open science0.0030.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0600.010

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 head0.196
GPT teacher head0.523
Teacher spread0.327 · 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 designQualitative
Domainnot available
GenreProtocol

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

Citations7
Published2021
Admission routes2
Has abstractno

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