First Thrive, Then Lead: An Emerging Approach to Engineering Leadership Education
Bibliographic record
Abstract
The study of human psychology has demonstrated that satisfying a set of basic psychological needs - autonomy, relatedness, and compentence - is essential for personal well-being and thriving. However, student mental health data across North America indicates that students are experiencing high levels of stress, anxiety, and depression - an indication that they are not thriving. Our experiences of traditional approaches to leadership education, and engineering leadership education by extension, is that it tends to focus largely on the development of competence-based needs, such as specific individual leadership skills and attributes. The lack of focus on satisfying student psychological needs of autonomy and relatedness means that current approaches to engineering leadership education may not be fully supporting and preparing students to thrive, and therefore lead. Our paper explores the possibilities of incorporating all three basic psychological needs essential to thriving through an expansive, transformational approach to engineering leadership education: First Thrive, Then Lead. Our emerging integrative and holistic approach to the development of engineering leadership education draws inspiration from traditional and non-European wisdoms and practices, as well as our personal lived experiences, and is grounded in well-established scientific theories.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".