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Record W4283216174 · doi:10.3390/educsci12060420

Role of Empathy in Engineering Education and Practice in North America

2022· article· en· W4283216174 on OpenAlex

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

VenueEducation Sciences · 2022
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEmpathyConceptualizationEngineering ethicsEngineering educationPoliticsDisciplinePolitical sciencePovertySociologyEnvironmental ethicsPublic relationsEngineeringPsychologySocial scienceEngineering managementComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Does engineering design education in North America prepare students to address the major issues of our time? In today’s political and social climate, engineers are part of multi-disciplinary teams tasked with finding solutions to complex issues like poverty, climate change, the housing affordability crisis, resource depletion, and water shortages. By definition, these problems are “wicked”. If engineers are to play a role in addressing issues that exist at the intersection of technology and society, they must have a deep understanding of both technical competencies and of human factors. They must have the ability to empathize. In consideration of today’s social, political, and environmental challenges, it has never been more important to instill social competencies into engineering education and practice, particularly around engineering design. This paper analyzes the previous literature on empathy in engineering education in North America and synthesizes the data to present the conceptualization that engineers have of empathy in education and practice.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.298
Teacher spread0.284 · 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