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Record W2895629017 · doi:10.1016/j.jtte.2018.10.001

Sustainable education for bridge engineers

2018· article· en· W2895629017 on OpenAlexaff
Paul Gauvreau

Bibliographic record

VenueJournal of Traffic and Transportation Engineering (English Edition) · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBridge (graph theory)EngineeringConstruction engineeringCivil engineeringStructural engineeringForensic engineeringMedicine

Abstract

fetched live from OpenAlex

This article examines the way we educate future bridge engineers from the perspective of sustainability. The term sustainability is used in its original sense, namely, a quality of a practice that can be continued long into the future because the benefits it creates outweigh its costs. The focus is on the relation between the way we educate future designers of bridges and the value created by the works they design. It is demonstrated that there are several significant flaws in the current curriculum, and recent efforts to inject more “design” into the curriculum are unlikely to bring about needed change. The article proposes incorporating specific elements of knowledge, skills, and values into the curriculum to increase its capacity to produce engineers who are competent and creative. The most important aspect of knowledge to be added to the curriculum is the knowledge obtained from the critical study of good completed bridges. The most important skills to be added are the use of drawing as a tool for enhancing creativity and the use of scientific principles as tools for validating new ideas. The most important values to be taught relate to the duty of the engineering profession to create value for society by designing every bridge to be an increment in a continual process of improvement on existing technology.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.205
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations7
Published2018
Admission routes1
Has abstractyes

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