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Record W4412870730 · doi:10.24908/pceea.2025.19603

Building a hybrid leadership development program for working engineers and technical talent

2025· article· en· W4412870730 on OpenAlexaffvenue
Emily Moore, Lydia Wilkinson, Estelle Oliva-Fisher, Jennifer Galley, Melissa Siah

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2025
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLeadership developmentEngineering ethicsEngineering managementEngineeringConstruction engineeringArchitectural engineeringSociologyManagementPolitical sciencePublic relationsEconomics

Abstract

fetched live from OpenAlex

A new leadership development program was developed in partnership with Vale Base Metals to support emerging technical leaders across their international organization. The leadership program was envisioned as a hybrid program with 6 asynchronous modules covering technical leadership, self-leadership, team skills, and effective communication. The content was piloted in a 3-day, fully in person version of the program. Informed by the experience of the pilot, the asynchronous modules were refined, and a second hybrid cohort was delivered. The modules were highly interactive. Each module had an accompanying workplace activity to apply the concepts and students submitted a follow-up reflection. This paper describes the program objectives, learning outcomes and activities. Instructor reflections were used to gather lessons learned from developing the modules and delivering the program. Reflections also include instructor observations on key competency gaps and needs expressed by the participants. The experience suggests that the leadership development approaches being used in engineering education are highly transferrable to professional development programs, and that there is great power in situating that learning in the workplace. The lessons learned can be applied to undergraduate and graduate instruction.

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)0.000
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 score0.298
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.220
Teacher spread0.210 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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