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Record W4405674769 · doi:10.24908/pceea.2024.18545

Opportunities and Obstacles for an Embedded STS Program in Engineering

2024· article· en· W4405674769 on OpenAlexafffundvenue
Alexi Orchard

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Waterloo
KeywordsEngineering ethicsEngineeringComputer scienceSoftware engineeringSystems engineeringEngineering management

Abstract

fetched live from OpenAlex

The “Embedded STS Program” is a model for teaching the ethics of engineering and technology via instructors from a Science and Technology Studies (STS) program housed directly within an Engineering department. Our institution, the University of Waterloo, has an embedded STS-like program under another name, the Centre for Society, Technology, and Values (CSTV). In this paper, we share the results of a qualitative survey completed by 43 students recruited from seven of the CSTV courses offered in 2022. These results provide context as to obstacles and opportunities in the embedded program related to curricular, institutional, and cultural dimensions. We conclude there is a need for resources and leadership to manage the curricular and cultural obstacles that hinder (1) students in applying knowledge on ethics and social impacts across educational and professional contexts and (2) institutions in drawing upon STS expertise for teaching engineering and technology ethics.

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)0.015
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0110.007
Scholarly communication0.0060.004
Open science0.0020.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.023
GPT teacher head0.264
Teacher spread0.240 · 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.

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

Citations1
Published2024
Admission routes3
Has abstractyes

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