Building the Engineering Mindset: Developing Sustainable Leadership and Management Competencies in First Year Engineering
Bibliographic record
Abstract
The broad inclusion of sustainable engineering leadership and management concepts are increasinglyrecognized as necessary to ensure the relevance of an engineering education in a rapidly shifting world.Engineering leadership and management are integral to the engineering mindset and necessary to addressthe complex engineering problems faced by society. Examples of these complex problems can be seen in theUN Sustainable Development Goals (SDGs) adopted by all UN member states, including Canada, in 2015[1]. The Canadian Engineering Accreditation Board (CEAB) identifies the need for strong non-technicalskills with a majority of the Graduate Attributes focusing on non-technical skills such as communication,teamwork, ethics and lifelong learning [2]. The UN SDGs are well aligned with the CEAB GraduateAttributes [3] and could be very effectively operationalized in engineering programs through the use of asustainable engineering leadership and management model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".