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Record W2737011048 · doi:10.36510/learnland.v10i2.796

Building From the Ground Up: The Key to Health and Well-Being in Schools

2017· article· en· W2737011048 on OpenAlexvenueaboutno aff
Sharon Klein

Bibliographic record

VenueLEARNing Landscapes · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsGeorge (robot)MindfulnessKey (lock)CurriculumCore curriculumCore (optical fiber)PedagogySociologyPsychologyMedical educationEngineeringMedicineComputer scienceArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

In this interview, Sharon Klein, the Head of School at St. George’s School of Montreal, discusses how health and well-being are integrated in the curriculum and school life. Since its beginning in 1930, St. George’s has operated on six founding principles, the first of which is “health comes first.” Ms. Klein describes how this principle is being applied—yoga, mindfulness, exercise literacy—and further describes how they have developed the Core 5 Program, based on current research, to meet the needs of today’s students. She concludes by sharing her vision of health and well-being in the future.

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)0.010
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.045
Scholarly communication0.0110.013
Open science0.0010.010
Research integrity0.0040.018
Insufficient payload (model declined to judge)0.0050.001

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 head0.303
Teacher spread0.275 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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