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Record W2969260162

Interview with Dr. Dawn Martin-Hill

2019· article· en· W2969260162 on OpenAlexvenueaboutno aff
M Kerr, Anna Kurdina

Bibliographic record

VenueUniversity of Toronto Medical Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousHarmonizationLibrary scienceSociologyTraditional knowledgeAnthropologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Dr. Dawn Martin-Hill holds a PhD in Cultural Anthropology and is one of the original foundersof the Indigenous Studies Program at McMaster University. She is the inaugural Paul R.McPherson Indigenous Studies Chair. Dr. Martin-Hill’s research is grounded in the principle thatsolution-based research in the area of Indigenous health must occur alongside building capacityfor community collaborations. She has embodied this principle through her numerouscommunity commitments: serving as Chair of the Indigenous Elders and Youth Council topromote the protection and preservation of Indigenous Knowledge systems, serving as an expertwitness on traditional medicines, and supporting reconciliation efforts to improve health servicesdelivery to First Nations through the “Harmonization of Traditional Medicine” in partnershipwith Six Nations Health Services. While working with communities, Dr. Martin-Hill has lednumerous grants funded by both the Social Sciences and Humanities Research Council (SSHRC)and Canadian Institutes of Health Research (CIHR) to conduct Indigenous knowledge researchfocused on Indigenous youth, women, language, ceremonies, traditional medicine and well-being.

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)0.003
metaresearch head score (Gemma)0.012
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: Other · Consensus signal: none
Teacher disagreement score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0050.015
Insufficient payload (model declined to judge)0.0310.008

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 head0.010
GPT teacher head0.257
Teacher spread0.247 · 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
GenreOther

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

Citations0
Published2019
Admission routes2
Has abstractyes

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Same venueUniversity of Toronto Medical JournalSame topicIndigenous Health, Education, and RightsFrench-language works237,207