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Record W2112894805 · doi:10.1109/pes.2004.1373040

An innovative industry-university partnership to enhance university training and industry recruiting in power engineering

2004· article· en· W2112894805 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Power Engineering Society General Meeting, 2004. · 2004
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsHydro-QuébecUniversité de SherbrookeMcGill University
Fundersnot available
KeywordsInternshipGeneral partnershipEngineering managementIncentiveEngineering educationTraining (meteorology)Power (physics)EngineeringBusinessFinanceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Summary form only given. Many universities have in the late 1990s reduced the number of courses in Power Engineering preferring to devote their resources to other areas such as information technologies. This comes at a time when it is predicted that a significant number of engineers are retiring in the next decade, in some utilities, more than a third. A leading utility in generation, transmission and distribution, has therefore taken the initiative, with the help of six local universities and the support of local industry, to create and finance an Institute of Electrical Power Engineering to train and recruit students, and allow universities to offer comprehensive programs in this discipline. Innovative aspects include the development of a specialized program of courses and laboratories common to participating universities, the active involvement of industry in program development and instruction and the introduction of incentives such as scholarships, industrial projects and internships.

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.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.003
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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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