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Record W1921240322 · doi:10.24908/pceea.v0i0.5946

A MODULE FOR TEACHING THE ROLE OF ETHICS IN SAFE PRACTICE

2015· article· en· W1921240322 on OpenAlexaffvenueabout
C. Flather, Douglas Ruth

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDutyPresentation (obstetrics)Engineering ethicsProfessional responsibilityScope (computer science)Ethical codeProfessional ethicsDisciplineEthical responsibilityEngineeringLegal ethicsProfessional conductEngineering managementComputer sciencePolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

The first duty of a professional engineer, emphasized in every professional act and code of ethics in Canada, is to protect the public. This duty extends from the design of structures, devices and processes that are safe and do not fail, to ensuring that structures, devices and processes are used in a safe manner. Engineers therefore have a duty to conduct their practice in an ethical manner, practicing only within their scope of competency, and taking personal responsibility for their works. Because the teaching of engineering is generally considered the practice of engineering, engineering educators have an ethical responsibility to ensure that students graduate with an understanding of what constitutes “ethical practice”. This presentation will describe a training module that allows students to explore ethically challenging situations through examination of case studies. The module is based on the Association of Professional Engineers and Geoscientists of Manitoba (APEGM) Code of Ethics and the cases that are presented are based on actual disciplinary cases.

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)0.001
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.197
Threshold uncertainty score0.658

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1970.077

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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
Published2015
Admission routes3
Has abstractyes

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