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Record W3041035892 · doi:10.1504/ijhes.2020.10030499

Planning to overcome perceived barriers: environmental and sustainability education, inclusion, and accessibility

2020· article· en· W3041035892 on OpenAlex
Laura Sims, Marie Élaine Desmarais

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Higher Education and Sustainability · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsUniversité de Saint-Boniface
Fundersnot available
KeywordsSustainabilityScope (computer science)Inclusion (mineral)Plan (archaeology)Universal Design for LearningUniversal designPedagogyEngineering ethicsComputer scienceMathematics educationPsychologyEngineeringGeographyEcology

Abstract

fetched live from OpenAlex

Integrating environmental and sustainability education (ESE) approaches into faculty of education classrooms that are inclusive of learners with a broad range of diverse needs can seem daunting. Our purpose is to reflect upon how to plan for inclusive learning experiences for all learners whilst also using ESE strategies to facilitate learning. Within the scope of a narrative enquiry, we explore how applying the universal design for learning principles when planning learning activities that use ESE approaches can help overcome perceived barriers. Community-based exemplary stories from our respective teaching practices are shared. We reflect upon potential benefits and implications for faculties of education when applying the universal design for learning and ESE approaches in teacher education programs. Future directions for research are proposed.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.373
Teacher spread0.361 · 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