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Record W7027458920

Culinary learning centre: using interior design to connect people and promote healthy living

2016· dissertation· en· W7027458920 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicSustainable Urban and Rural Development
Canadian institutionsnot available
Fundersnot available
KeywordsPracticumInterior designPopulationValue (mathematics)Social learningHealthy foodBehaviour change
DOInot available

Abstract

fetched live from OpenAlex

Obesity within the Canadian population is on the rise, as people continue to lead hurried lifestyles there is little time to slow down and prepare a meal for oneself or one’s family. As younger generations grow up in these rushed lifestyles, the opportunities to learn about food and nutrition in the home is quickly disappearing. As a result this practicum project explores promoting healthy lifestyles by reconnecting people with food through the creation of a Culinary Learning Centre located in The Forks Market Building, in Winnipeg, Manitoba. Individuals who are motivated to change the way they view and value food are provided with a comprehensive learning environment that approaches teaching through a holistic and collaborative manner. Informed by an extensive literature review into the Slow Food Movement, and learning theory; research into four case studies; and detailed programming, culminated in an interior design solution promoting positive learning, social interaction, and student 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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0040.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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
Published2016
Admission routes1
Has abstractyes

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