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Record W1941412156 · doi:10.24908/pceea.v0i0.5811

Comparison of Career Success Competencies and Engineering Leadership Capabilities

2015· article· en· W1941412156 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOpenness to experienceEngineering educationPsychologyPersonalityLifelong learningGlobalizationEngineering ethicsPedagogyMedical educationEngineeringManagementPolitical scienceEngineering managementSocial psychology

Abstract

fetched live from OpenAlex

Societal expectations of twenty-first century engineers have dramatically changed over the past few decades. There is a need to educate engineers not just in technical subjects, but also in many non-technical areas including globalization, communication, and leadership. There has been a growth of engineering leadership education programs offered by postsecondary engineering institutions. The effectiveness of these programs is often measured by the student’s acquisition of skills, without considering the benefit of these skills on the students’ careers. Using the career success competencies model, this paper seeks to determine if engineering leadership education impacts career success. The analysis showed a high amount of correlation with engineering leadership capabilities, indicating a positive relationship between engineering leadership education and career success. The most significant competencies related to an engineer’s career success were career insight, proactive personality, openness to experience, and lifelong learning

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.801

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 head0.031
GPT teacher head0.226
Teacher spread0.195 · 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