Building a hybrid leadership development program for working engineers and technical talent
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".