Student Perspectives on Socio-technical Thinking, Depoliticization, and Meritocracy
Bibliographic record
Abstract
Engineers are often asked to solve problems that involve socio-technical and political considerations. However, engineering education focuses more on technical than socio-technical concerns, contributing to a culture of disengagement. This study investigated whether second-year engineering student perspectives aligned with the culture of disengagement. Forty students responded to the survey on socio-technical thinking, depoliticization, and meritocracy. Students demonstrated some alignment with the culture of disengagement. They emphasized the technical work of engineering, identified politics as separate from engineering work, and generally agreed with prescriptive meritocracy. However, students also had perspectives that did not align with the culture of disengagement. Students were less in agreement with descriptive meritocracy and showed appreciation for considering multiples perspectives, encompassing cultural beliefs, social justice, and human emotions, in engineering work, and the importance of working with non-engineers. Future work will examine student perspectives throughout their engineering education to learn how they change with time.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".