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Record W2337611556 · doi:10.1016/s0140-6736(16)00345-7

Indigenous and tribal peoples' health (The Lancet–Lowitja Institute Global Collaboration): a population study

2016· article· en· W2337611556 on OpenAlexaff
Ian Anderson, Bridget Robson, Michele Connolly, Fadwa Al‐Yaman, Espen Bjertness, Alexandra King, Michael Tynan, Richard Madden, Abhay Bang, Carlos Ε. A. Coimbra, M. Amalia Pesantes, Hugo Amigo, Sergey Andronov, Blas Armién, Daniel Ayala Obando, Per Axelsson, Zaid Bhatti, Zulfiqar A Bhutta, Peter Bjerregaard, Marius B Bjertness, Ann Ragnhild Broderstad, Patricia Bustos, Virasakdi Chongsuvivatwong, Jiayou Chu, Deji, Jitendra Gouda, Harikumar Rachakulla, Thein Thein Htay, Aung Soe Htet, Chimaraoke Izugbara, Martina Kamaka, Malcolm King, Mallikharjuna Rao Kodavanti, Macarena Lara, Avula Laxmaiah, Claudia Patricia Henao Lema, Ana María León Taborda, Tippawan Liabsuetrakul, Andrey A. Lobanov, Marita Melhus, Indrapal I. Meshram, J. Jaime Miranda, Thet Thet Mu, Nimmathota Arlappa, Andrey I. Popov, Ana María Peñuela Poveda, Faujdar Ram, Hannah Reich, Ricardo Ventura Santos, Aye Aye Sein, Chander Shekhar, Lhamo Yangchen Sherpa, Peter Sköld, S. Tano, Asahngwa Constantine Tanywe, Chidi Ugwu, Fabian O. Ugwu, Patama Vapattanawong, Xia Wan, James R. Welch, Gonghuan Yang, Zhaoqing Yang, Leslie Yap

Bibliographic record

VenueThe Lancet · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Health and Education
Canadian institutionsSickKids FoundationInstitute of Indigenous Peoples' HealthSimon Fraser University
FundersMedical Research Council
KeywordsIndigenousPolitical sciencePopulationMedicineGeographySocioeconomicsEconomic growthEnvironmental healthSociologyBiology

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.079
GPT teacher head0.446
Teacher spread0.367 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations996
Published2016
Admission routes1
Has abstractno

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