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Record W4403764043 · doi:10.24908/pceea.2023.17108

Understanding Engineers’ Ethical Environmental Responsibility

2024· article· en· W4403764043 on OpenAlexaffvenueabout
Emma Jane Randall, David R. Strong

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsEngineering ethicsEthical responsibilityEnvironmental ethicsPolitical scienceEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Engineers Canada states that engineers must, “Hold paramount the safety, health and welfare of the public and the protection of the environment and promote health and safety within the workplace” [1]. This is frequently the only guideline directly related to the environment included in Canadian provincial and territorial Professional Engineering Codes of Ethics (PECoE) which are a primary resource for teaching engineering ethics [1]; notably, Ontario’s PECoE currently does not explicitly mention the environment [2]. Minimal formal ethical guidelines indicate interpretations of engineers’ professional responsibility with respect to the environment may vary in both industry and engineering education. With the growing focus on sustainable development alongside escalating environmental crises, a thorough investigation of engineers’ professional ethical responsibility to the environment is time-sensitive and necessary [3]. The purpose of this study is to improve understanding of how engineers and engineering students view and interpret their professional ethical responsibility to the environment and PECoE in a Canadian context. The ultimate goal of this research will be to aid the development of engineering ethics and sustainability curricula, as well as Professional Engineering Codes of Ethics. A key result from preliminary findings, is that participants feel there may be a need to explicitly include more guidelines on the environment in the Ontario PECoE.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.996

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.204
Teacher spread0.191 · 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
Published2024
Admission routes3
Has abstractyes

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