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Record W2133805338 · doi:10.1109/istas.2000.915569

Improving the bridge: making engineering education broader and longer

2002· article· en· W2133805338 on OpenAlexaff
Robert Hudspith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPresentation (obstetrics)Engineering educationBridge (graph theory)Value (mathematics)Set (abstract data type)Work (physics)Medical educationEngineeringPsychologyMathematics educationPedagogyEngineering ethicsEngineering managementComputer scienceMedicineMechanical engineering

Abstract

fetched live from OpenAlex

In response to the need to prepare engineers to be aware of and sensitive to the cultural, political, and social aspects of their work, McMaster University began a five-year Engineering and Society Programme in 1991. The complete technical education in a selected engineering discipline is supplemented with a set of seven courses which examine the complex interactions between technology and society. In addition, a series of focused elective courses are taken outside of the Faculty. The viability and success of the programme have been assessed through extensive surveys of in-course students, alumni and Engineering faculty members. Findings show that this Programme attracts a disproportionate number of females and, in general, students who prefer 'deep' learning. The Programme has been well received by students. Especially valued are: the freedom to take courses outside of Engineering; the learning of critical thinking, writing, and oral presentation skills; and the sense of community experienced. Overall assessment of the value of the Programme to the Faculty by faculty members is high.

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)0.009
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.005
Scholarly communication0.0090.024
Open science0.0030.019
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0530.009

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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 designTheoretical or conceptual
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

Citations1
Published2002
Admission routes1
Has abstractyes

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