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Record W2886029561 · doi:10.24908/pceea.v0i0.10203

HELPING ENGINEERS DEVELOP AND EXERCISE CREATIVE MUSCLES

2018· article· en· W2886029561 on OpenAlexaffvenue
Zbigniew J. Pasek

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCreativityCognitive reframingMultinational corporationIBMFrame (networking)Subject (documents)Engineering ethicsComputer scienceKnowledge managementEngineeringPsychologyBusiness

Abstract

fetched live from OpenAlex

Abstract – A recent IBM-conducted survey of CEOs of multinational corporations indicated that creativity trumps other leadership characteristics. Across industries, organizations operate in increasingly complex and uncertain environments. Existing solutions are quickly exhausted or become obsolete, thus replacing them requires continuous innovation. Teaching existing solutions, the mainstay of formal education, is not enough. Students must learn how to consciously frame and reframe problems, create new knowledge and generate creative solutions on an ongoing basis. Most important, students must learn ways to motivate themselves to recognize and seek out problems as opportunities for generating creative solutions. While in higher education importance of creativity is recognized, practical implementation of teaching it is still an afterthought or a sideline. While creativity as an academic subject is somewhat elusive, in particular in engineering education, recent accumulation of knowledge enables implementation of more systematic approaches.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.011

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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 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
Published2018
Admission routes2
Has abstractyes

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