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Record W3048306660 · doi:10.13140/rg.2.2.18019.58400

Restoring Nature Literacy: Developing a Nature-Based Afterschool Program to Restore Connection Between Children and their Local Natural Environment

2019· article· en· W3048306660 on OpenAlexaff
Michelle Barrette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDisconnectionExperiential learningNatural (archaeology)PsychologyLiteracyOutdoor educationPedagogyGeographyPolitical science

Abstract

fetched live from OpenAlex

Children are spending significantly less time outdoors compared to children of previous generations. Increase use of technology, lack of nearby natural areas, and concerns for risk and safety have all contributed to a generation of children spending much of their time indoors. As a result, a disconnection between children and nature has occurred. Research suggests benefits of spending time in nature include increased physical activity, reduced stress, and development of a fondness for nature. The purpose of my project was to develop an experiential nature-based afterschool program for school-aged children. My hope was to restore fading ecological knowledge by reconnecting children with their local natural environment through a series of outdoor physical and social activities. Feedback at the end of the project suggested children acquired new ecological knowledge and skills while enjoying their time spent in nature. Success of project could be used to inform future nature-based after school programs.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.006
GPT teacher head0.245
Teacher spread0.239 · 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
Published2019
Admission routes1
Has abstractyes

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