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Record W3173804194 · doi:10.24908/pceea.vi0.14960

USING ACTOR NETWORK THEORY TO EXPLORE SUSTAINABILITY ISSUES IN AN ENGINEERING & SOCIETY COURSE

2021· article· en· W3173804194 on OpenAlexaffvenue
Robert Irish, Lisa Romkey

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSociotechnical systemMultidisciplinary approachSustainabilityComputer scienceEngineering managementEngineeringManagement scienceSociologyKnowledge managementSocial science

Abstract

fetched live from OpenAlex

This paper explores the use of Actor-Network Theory as a tool for exploring the complexity ofsustainability issues in a core Engineering and Society course for second-year students in a large,multidisciplinary engineering program. In the course, Actor Network Theory, which is a method for analyzing sociotechnical issues with an emphasis on the concept of power and its distribution, was introduced to the students through a series of learning activities and an assignment, initially encouraging the students to apply the approach to a system within their own life. Subsequently, the approach was used to analyze complex sociotechnical issues, for example, the use of Coal-based energy in Nova Scotia, and the Coastal Gaslink pipeline dispute in the Wet’suwet'enterritory. This paper describes our approach to introducing Actor Network Theory to engineering students, the benefits and limitations of the approach, and the efficacy of the approach for exploring sustainability issues. Other instructors may consider the introduction of ActorNetwork Theory through courses in Engineering & Society and Engineering Design.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.031
GPT teacher head0.339
Teacher spread0.308 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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