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Record W2328591682 · doi:10.1080/13504622.2016.1153046

Immigrant children promoting environmental care: enhancing learning, agency and integration through culturally-responsive environmental education

2016· article· en· W2328591682 on OpenAlexaffabout
Natasha Blanchet‐Cohen, Rosemary C. Reilly

Bibliographic record

VenueEnvironmental Education Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnvironmental educationAgency (philosophy)ImmigrationSociologyFocus groupParticipatory action researchSense of agencyPedagogyPublic relationsPsychologyPolitical scienceSocial psychologySocial science

Abstract

fetched live from OpenAlex

This paper examines the potential of culturally-responsive environmental education to engage immigrant early adolescents. Our study suggests that environmental involvement can become a means and an end for children to bridge their school and home in agential ways. Drawing from a multi-phase study involving focus groups with children, parents, and teachers from three culturally-diverse schools in Montreal, as well as a green action research project, we examine children’s role as environmental educators and ambassadors. The role of environmental ambassador allowed children to take on positions that departed from conventional parent-child social scripts, and enhanced the communication between school-student-home, between generations, and spoke to their sense of place. We contend that culturally-responsive environmental education offers a unique space for enacting democracy, knowledge creation and integration, but this opportunity is often squandered. Bi-directional, responsive, and consistent home-school-community-place relations need to be actively supported.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.008
GPT teacher head0.300
Teacher spread0.292 · 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; both teacher heads agree on what is shown here.

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

Citations37
Published2016
Admission routes2
Has abstractyes

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