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Record W2018314500 · doi:10.5206/wurjhns.2014-15.5

Western Faculty Profile: Dr. Shauna Burke

2014· article· en· W2018314500 on OpenAlexaffvenueabout
Natalie Visca

Bibliographic record

VenueWestern Undergraduate Research Journal Health and Natural Sciences · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsWestern University
Fundersnot available
KeywordsBreastfeedingChildhood obesityHealth promotionMedical educationPhysical activityPoliticsPromotion (chess)PsychologySociologyGerontologyMedicineObesityPolitical sciencePublic healthNursingPediatricsOverweight

Abstract

fetched live from OpenAlex

Dr. Shauna Burke is an Assistant Professor at Western University in the Faculty of Health Sciences. She teaches both undergraduate and graduate level courses and has been recognized several times for her outstanding contributions to teaching. Dr. Burke is a faculty researcher in the Health Promotion Laboratory at Western and was the Principal Investigator of the Children’s Health and Activity Modification Program (“C.H.A.M.P.”), a family based program targeting childhood obesity in London, Ontario. Her current research interests include childhood obesity, health behaviours (physical activity, breastfeeding), and literacy (food, physical, political).

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.333
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.3330.176

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 head0.172
GPT teacher head0.523
Teacher spread0.351 · 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.

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
Published2014
Admission routes3
Has abstractyes

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