Cultivating solidarity for action on social justice in engineering
Bibliographic record
Abstract
As graduate students, we have witnessed and experienced firsthand how engineering education, due to engineering culture, can perpetuate harm and enhance systemic oppression and inequality in society. Documenting our efforts to counteract this status-quo, we share our individual and collective experience working to center social justice in engineering education. Using collaborative autoethnography, we qualitatively explore, through self-reflection, how we sought to integrate social justice into engineering education and developed a praxis of engineering social justice. Our group’s collaboratively developed praxis of engineering social justice seeks to overcome institutional and individual barriers to an integration of social justice in engineering practice by 1) fostering a reflexive practice through values and positionality, 2) engineering space for inclusive collaboration, and 3) seeing justice as a necessary lens for engineering education. Through this analysis of our personal experience, we hope to motivate and challenge readers to develop a praxis of engineering social justice that will inform their actions in this space.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".