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Record W1818136897 · doi:10.24908/pceea.v0i0.4662

CASE STUDIES – A TOOL FOR LEARNING

2012· article· en· W1818136897 on OpenAlexaffvenueabout
Jim Graham, David Kurz

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAccreditationTeamworkSubject (documents)Engineering managementUndergraduate educationEngineeringEngineering educationComputer scienceEngineering ethicsMedical educationManagementWorld Wide Web

Abstract

fetched live from OpenAlex

The Canadian Engineering Accreditation Board requires broadly-based undergraduate programs that develop technical awareness, motivation, confidence, teamwork and life-long learning. These objectives can be reached in both undergraduate and postgraduate courses using projects based on case studies. The projects use published data and records of performance that students can reproduce using modern design tools and numerical analysis. The paper includes examples of projects in the subject area of geotechnical engineering. Case studies are becoming increasingly valuable as employers and licensing authorities move toward a combination of broadly-based undergraduate education and more specialized postgraduate or in-practice training.

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)0.001
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 score0.314
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.225
Teacher spread0.216 · 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

Citations2
Published2012
Admission routes3
Has abstractyes

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