Bibliographic record
Abstract
The murder of George Floyd catalyzed global awareness of systemic racism and reignited calls for diversity and inclusivity, including in engineering. It highlighted how the legacies of slavery and colonialism persist through neoliberalism and racial capitalism. However, this momentum has waned, with backlash threatening the rollback of critical equity efforts. Superficial inclusivity initiatives are insufficient, but abandoning them entirely risks perpetuating historical and ongoing harms. Current approaches to fostering awareness of colonial impacts, particularly in engineering, inadequately prepare students to meaningfully engage with systemic inequities. We present First Thrive, Then Lead, a transformative framework for engineering education emphasizing mental health and well-being—for educators and students—as foundational to decolonization. This approach addresses how colonial legacies manifest in engineering and critiques dominant paradigms, such as socio-technical dualism, meritocracy, and depoliticization, which hinder meaningful engagement with systemic inequities. Through classroom and extracurricular applications, we demonstrate the potential of First Thrive, Then Lead to foster leadership rooted in empathy, systemic awareness, and transformational change. We call on the engineering community to confront its complicity in structural inequities and adopt practices that prioritize collective healing and justice, essential for the future of engineering education. Updated with minor typographical corrections: June 30, 2025.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".