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Record W1760061824 · doi:10.24908/pceea.v0i0.4820

DEVELOPING ENGINEERING MANAGERS: THE MASTER OF ENGINEERING MANAGEMENT PROGRAM AT MEMORIAL UNIVERSITY OF NEWFOUNDLAND

2013· article· en· W1760061824 on OpenAlexaffvenueabout
Amy Hsiao

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMandateEngineering managementProgram managementEngineeringGraduate studentsEngineering educationMedical educationEngineering ethicsProject managementPolitical scienceMedicineSystems engineering

Abstract

fetched live from OpenAlex

The Master of Engineering Management (MEM) program in the Faculty of Engineering and Applied Science (FEAS) at Memorial University of Newfoundland (MUN) is one of the first designated MEM graduate programs in Canada. With a mandate to attract both local and international students from all engineering disciplines, and to provide balanced graduate training in Management and Engineering, the program has experienced increasing growth since it welcomed its first cohort in 2009. For the purpose of knowledge sharing and discourse, this paper will present details of the program and student characteristics, and discuss outcome assessment and future program planning.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.206
Teacher spread0.196 · 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 designObservational
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

Citations11
Published2013
Admission routes3
Has abstractyes

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