Understandings of Social Justice in Engineering Education
Bibliographic record
Abstract
Despite being a core element in the engineers’ code of ethics, social justice is rarely discussed in engineering classrooms. International service-learning experiences offer opportunities to explore complex, socially relevant problems. Seven participants’ responses to surveys administered before and after an engineering design experience were analyzed to assess the participants’ understanding of social justice. Employing Leydons and Lucena’s six social justice criteria as a framework, we found all six criteria present in the participants’ responses, with all participants demonstrating contextual listening. Four participants’ responses aligned with all six criteria, indicating their desire to discuss social justice topics. Seven sub-themes were identified and include empathy, interest in people and culture, human-centered design, root cases, limited resources, personal agency, and the role of the engineering profession. The participants advocated for human-centered design, but their language did not include the community as co-designers. Also, the participants identified the complex political and economic nature of real-world problems, and they developed their engineering identities through the experience. This exploratory study seeks to encourage engineering educators to facilitate social justice discussions and to offer ill-structured, real-world problems to students.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".