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Record W7000424299

Enhancing Child-Led learning in a Montessori Environment through Biophilic Design

2017· dissertation· en· W7000424299 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation Methods and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPracticumInterior designMontessori methodWork (physics)Space (punctuation)Learning environmentDesign elements and principlesIndependence (probability theory)Level design
DOInot available

Abstract

fetched live from OpenAlex

This practicum blurs the interior boundaries through the design of an enhanced Montessori environment in Winnipeg, Canada. Through the study of Forest Kindergartens and Biophilic Design, interior and exterior space work cohesively together to provide an alternative learning environment. By fusing the interior with exterior, the Montessori philosophy is designed for in an innovative way, while simultaneously implementing the benefits of Biophilic Design and Forest Kindergartens. Through a comprehensive literature review, precedent studies, and a conceptual exploration, an enhanced program and design concept have been established to apply the theoretical framework to the built environment. As a result, the foundational principles surrounding the Montessori philosophy and Forest Kindergartens have been designed for, using Biophilic Design as a catalyst. The final concept truly supports the Montessori philosophy, instilling independence through community, hands on learning, and risk through every aspect of the design.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.161
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.004
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.045
GPT teacher head0.313
Teacher spread0.268 · 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 designQualitative
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 routes1
Has abstractyes

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