MétaCan
Menu
Back to cohort
Record W2752231119 · doi:10.47678/cjhe.v47i2.186284

Teaching Sustainability in Higher Education: Pedagogical Styles that Make a Difference

2017· article· en· W2752231119 on OpenAlexafffundvenue
Carol Scarff Seatter, Kim Ceulemans

Bibliographic record

VenueCanadian Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersGoldcorp
KeywordsTransformative learningPrerogativeSustainabilityPedagogyCurriculumSociologyHolistic educationHigher educationCritical thinkingCitizen journalismEngineering ethicsMathematics educationPsychologyPolitical scienceEngineeringPoliticsEcology

Abstract

fetched live from OpenAlex

The challenge of teaching sustainable development in higher education can mean that students—as future citizens—are left without insight, commitment, or a sense of their position regarding meaningful beliefs and actions related to sustainability. A paradox arises when educators approach a sustainability curriculum that has the potential to transform students’ thinking and actions, with a reductive and non-substantive pedagogy. This paper uses an epistemological and pedagogical analysis of relevant literature to redefine, clarify, and provide a more systematic and holistic understanding of a transformative pedagogy required for learning. The central thesis juxtaposes three sustainability curricular positions with three pedagogical models that vary decidedly in their emphasis on the prerogative of the learner’s prior knowledge and beliefs, the engagement of the learner, and the potential for critical thinking and transformative learning. It is found that a transformative pedagogy overcomes and eliminates the paradox, helping societies become more sustainable.

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)0.012
metaresearch head score (Gemma)0.017
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 score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0110.006
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.108
GPT teacher head0.424
Teacher spread0.317 · 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

Citations151
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Higher EducationSame topicSustainability in Higher EducationFrench-language works237,207