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Record W2617113318

Implementing a Nature-Based Approach in Elementary Schools

2017· other· en· W2617113318 on OpenAlexaffabout

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typeother
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMathematics educationComputer sciencePedagogySociologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

The Master of Teaching Research Project is a qualitative study that addresses the topic of implementing a nature-based approach in elementary schools. The existing literature highlights the benefits of exposing young children to nature, and suggests possible downfalls if children do not have opportunities to meaningfully engage with the outdoors and natural materials. However, much of the literature focused on an early childhood setting instead of a school environment. With this in mind, the main research question that guided this study was: How does a small sample of elementary teachers implement nature-based learning with their students? Data was collected through semi-structured interviews with two elementary school teachers currently working in Ontario. Findings suggest that a nature-based approach can be integrated into a range of schools, regardless of the school environment. In addition, nature-based educators from this study addressed ways in which teachers can incorporate the outdoors along with natural materials, while still connecting these experiences to the Ontario curriculum. Findings also show that a teacher’s perceptions of the outdoors and their willingness to incorporate nature-based experiences play a significant role. Implications for the educational community and personal practice are discussed, and recommendations are made for school boards, educators, parents/caregivers, as well as areas for further research in this field.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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: Other
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1530.000

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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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 teacher head, 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

Citations3
Published2017
Admission routes2
Has abstractyes

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