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Record W4291792386 · doi:10.1038/s41591-022-01944-7

Open Letter to G7 and G20 leaders: resolve global crises to secure our future

2022· letter· en· W4291792386 on OpenAlexafffund
Shashika Bandara, Prativa Baral, Anshumi Joshi, Joy Muhia, Afifah Rahman-Shepherd, Praju Adhikari, Alice Bayingana, Hloni Bookholane, Yara Changyit-Levin, Sara Dada, Rohini Dutta, Mohammad Yasir Essar, Nelson Aghogho Evaborhene, Daniel Krugman, Ramya K. Kumar, Malvikha Manoj, Kedest Mathewos, Nehemiah Olson, Rhiannon Osborne, Daniel Romero-Álvarez, Zaw Myo Tun, Brian Li Han Wong

Bibliographic record

VenueNature Medicine · 2022
Typeletter
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcGill UniversityTrent UniversityMcGill University Health Centre
FundersFaculty of Medicine and Health, University of SydneyJohns Hopkins Bloomberg School of Public HealthUniversity College DublinNational University of SingaporeUniversity of MinnesotaTrent UniversityMcGill UniversityUniversity of Cape TownUniversity of WashingtonJohns Hopkins University
KeywordsPolitical sciencePublic relationsMedicine

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)0.007
metaresearch head score (Gemma)0.040
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.183
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0100.006
Open science0.0030.007
Research integrity0.1830.090
Insufficient payload (model declined to judge)0.0260.017

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 head0.027
GPT teacher head0.363
Teacher spread0.335 · 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
GenreCommentary

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

Citations3
Published2022
Admission routes2
Has abstractno

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