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Record W3002978823 · doi:10.24908/pceea.vi0.13811

INSTILLING ENGINEERING STUDENTS WITH SOCIAL RESPONSIBILITY: TECHNOLOGICAL STEWARDSHIP

2019· article· en· W3002978823 on OpenAlexaffvenue
Katherina V. Tarnai-Lokhorst

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStewardship (theology)ObligationMandateSocial responsibilityEngineering ethicsEngineeringEngineering educationPublic relationsEngineering managementPolitical science

Abstract

fetched live from OpenAlex

Stewards of the implementation of technology in society, engineers regularly balance innovative design with their primary mandate: protection of the public interest. As technological stewardship achieves higher priorities within the requirements of engineering education, students must learn to acknowledge their obligation to society by deeply reflecting on the ethical implications of engineering design. Mech410T– Engineering in Society: Technological Stewardship is a new, fully-online course that guides students through a comprehensive assessment of this obligation using case study analyses, small group discussions, and team-based, project-based learning. The module activities consist of assigned readings; video recordings of the topic overview and a series of interviews with key partners within the engineering community, including practitioners, stakeholders and regulators; module quizzes; discussion posts; and a term paper, researched and written as a team.

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)0.007
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.008
Scholarly communication0.0110.006
Open science0.0010.013
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0140.005

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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