Engineered Violence: Confronting the Neutrality Problem and Violence in Engineering
Bibliographic record
Abstract
Engineering educators continue to challenge the social/technical dichotomy by framing engineering as a set of non-neutral activities. Faced with the historical realities that engineers are often “hired-guns” for the military interventions and capital accumulation, educators have sought to establish new canons for engineering ethics that are based on paradigms of peace and critically engaged pedagogies. We aim to situate nuanced understandings of violence—as understood by 21st century social movements—into the larger goal of reorienting engineering ethics for a more peaceful and socially just world. Literature is presented about particularly challenging what we identify as the “neutrality problem” in engineering education. We argue that theories of interpersonal and structural violence will better help engineers confront the neutrality problem in classrooms and workplaces. Our ultimate goal is to open up a larger research agenda on violence for engineering educators and practitioners.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".