MétaCan
Menu
Back to cohort
Record W2954239064 · doi:10.1136/bmjgh-2019-001693

Make visible the invisible: innovative strategies for the future of global health

2019· editorial· en· W2954239064 on OpenAlexaff
Muriel Mac-Seing, Déborah Le Nogue, Cheick Oumar Bagayoko, Amy Sy, Jean-Luc Dumas, Willy Dunbar, Claude Sicotte, Bernard Nordlinger, Jean-Paul de Gaudemar, Rachida Maouche, Antoine Flahault

Bibliographic record

VenueBMJ Global Health · 2019
Typeeditorial
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsFrancophone University AssociationUniversité de Montréal
Fundersnot available
KeywordsPublic healthGlobal healthPolitical sciencePublic relationsBusinessEnvironmental healthMedicineNursing

Abstract

fetched live from OpenAlex

innovative strategies for the future of global health.

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)0.020
metaresearch head score (Gemma)0.046
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 score0.022
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.046
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.002
Science and technology studies0.0050.015
Scholarly communication0.0220.020
Open science0.0040.004
Research integrity0.0220.035
Insufficient payload (model declined to judge)0.0090.006

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 head0.020
GPT teacher head0.414
Teacher spread0.394 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Global HealthSame topicGlobal Health and SurgeryFrench-language works237,207