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Record W7134225156 · doi:10.21606/drslxd.2025.083

Decolonizing the Engineering Designer

2025· article· W7134225156 on OpenAlexaff
Leslie Wexler, Matt Borland, Kate Sellen

Bibliographic record

Venuenot available
Typearticle
Language
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of WaterlooGeorge Brown College
Fundersnot available
KeywordsTransformative learningConversationIndigenousGratitudeEngineering educationProcess (computing)EmpathyAutoethnography

Abstract

fetched live from OpenAlex

In a dynamic conversation between three design educators, the group explores their experiences in decolonizing education and fostering transformative learning environments. Their discussion emphasizes nurturing the "whole self"—intellectual, emotional, physical, and spiritual dimensions. The educators reflect on how to integrate holistic practices into a graduate level Engineering design reading course (SYDE760 - Decolonizing Engineering Design) that challenged traditional approaches which prioritize technical skills and expertise. Instead, their goal was to balance all aspects of the self, creating spaces where students could authentically engage and grow. The conversation highlights practical classroom experiences associated with Indigenous pedagogies and traditional knowledges, such as storytelling, land-based learning, and ceremonies, as tools for fostering connection and transformation. A pivotal moment they shared was a tobacco tie ceremony, where students reflected on gratitude and their relationships with the land and each other. These experiences allowed students to move beyond technical discussions, immersing themselves in practices that bridged the intellectual with the spiritual and emotional. Throughout their dialogue, the educators emphasize that decolonizing design education is not a prescriptive process but a relational journey.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.297
Teacher spread0.286 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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