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Record W2033320218 · doi:10.1080/13504622.2012.729812

Negotiating the constraints of schools: environmental education practices within a school district

2012· article· en· W2033320218 on OpenAlexaff
Xavier Fazio, Douglas D. Karrow

Bibliographic record

VenueEnvironmental Education Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsBrock University
Fundersnot available
KeywordsEnvironmental educationNegotiationCurriculumCertificationPedagogyNormativeFocus groupSociologyPolitical sciencePublic relationsPsychologySocial science

Abstract

fetched live from OpenAlex

The purpose of this study was to explore environmental education (EE) practices within elementary and secondary schools. Using complementary mixed-methods (survey and focus groups), we detail these practices in schools (n = 58) within one school district. Our findings are categorized according to classroom teaching conditions affecting EE, and whole-school perspectives of the supports and resources for EE in these schools. Our analyses reveal that while typical normative teaching and cultural constraints of schools are still evident (e.g. curriculum standards, school-level organization), there are identifiable practices involving administrators and teachers negotiating these challenges due to their personal commitment to schools and the environment. In particular was a provincial environmental certification program called Ecoschools supporting environmental educators’ initiatives at their respective schools. We conclude with a discussion of recommendations based on an interpretation of our findings in relation to the school reform literature on how to enhance EE in schools and propose future research opportunities.

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)0.003
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0090.004
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.375
Teacher spread0.343 · 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

Citations36
Published2012
Admission routes1
Has abstractyes

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