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Record W4385294129 · doi:10.3389/feduc.2023.1177035

Stubborn boundaries: the iron ring ritual as a case of mapping, resisting, and transforming Canadian engineering ethics

2023· article· en· W4385294129 on OpenAlexaffabout
R. Paul, Kari Zacharias, Edmund Martin Nolan, Kyle Monkman, Victoria Thomsen

Bibliographic record

VenueFrontiers in Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of TorontoUniversity of ManitobaUniversity of Calgary
Fundersnot available
KeywordsFraming (construction)ScholarshipDialecticSociologyColonialismAgency (philosophy)Engineering educationEngineering ethicsEnvironmental ethicsAestheticsEngineeringPolitical scienceEpistemologySocial scienceLawPhilosophyCivil engineering

Abstract

fetched live from OpenAlex

This article explores the historical context and ongoing discussions of the iron ring ritual, a prominent tradition in Canadian engineering. We employ discourse analysis to describe and analyze components of the ritual itself, as well as more recent texts related to contemporary conversations about the ritual. We apply Alice Pawley’s scholarship on boundary work in engineering as an analytical framework and find the ritual has served to reproduce and map boundaries around engineering ethics and responsibility in Canada, and numerous actors have resisted those boundaries based on opposition to the colonial, misogynistic, and Christian values embedded in the ritual, as well as the ritual’s framing of engineering agency and responsibility. We reflect on the lessons this case can offer for members of the Canadian engineering and engineering education communities, as well as for those interested in the power and complexity of humanistic interventions in engineering.

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)0.014
metaresearch head score (Gemma)0.024
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 score0.918
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0820.098
Scholarly communication0.0140.006
Open science0.0040.012
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.284
Teacher spread0.261 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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