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Record W4412870790 · doi:10.24908/pceea.2025.19667

Integrating Climate Change Considerations into Geotechnical Curricula

2025· article· en· W4412870790 on OpenAlexafffundvenue
Sophie Jung, Veronique Gisondi, Anouk Desjardins, Antoine B. Jacquey, Benoît Courcelles, Pooneh Maghoul

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2025
Typearticle
Languageen
FieldEngineering
TopicCivil and Structural Engineering Research
Canadian institutionsPolytechnique Montréal
FundersPolytechnique Montréal
KeywordsCurriculumGeotechnical engineeringClimate changeGeologyEngineeringCivil engineeringEnvironmental scienceSociologyPedagogy

Abstract

fetched live from OpenAlex

Despite increasing awareness of climate change, most geotechnical engineering curricula still focus primarily on designing durable structures based on limit state principles, overlooking sustainability and long-term climate resilience. This paper explores how to integrate sustainability and climate change considerations into geotechnical engineering education, highlighting the differences between limit state-oriented teaching and the necessity for sustainable design. A two-phase strategy is proposed: (1) creation of a database of real-world case studies illustrating climate impacts on geotechnical works; (2) development of graduate-level courses that deepen knowledge of sustainable geotechnics as well as climate geotechnics. By systematically embedding climate-related case studies in existing courses, students gain an expanded view of design variables. The new courses foster a comprehensive skill set centered on sustainability, climate resilience, and interdisciplinary collaboration. The proposed reforms aim to produce geotechnical engineers capable of designing infrastructure that is both climate-resilient and sustainable in a changing climate.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.231
Teacher spread0.224 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2025
Admission routes3
Has abstractyes

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