Becoming Aware of Engineering Culture: Toward Sculpting a New Way of Acting, Being, and Thinking in the World
Bibliographic record
Abstract
In this paper the problem of “engineering culture” is explored through the lens of Cultural Anthropology. A literature review of papers which have examined engineering culture in various university environments was conducted and preliminary conclusions drawn. Anthropologists define culture as how groups and individuals respond to dominant images. Of the various images in the literature, the image of the engineer as “problem solver” is most helpful in diagnosing the issues with engineering culture – a culture that has been found to lack in promoting self awareness, political awareness, understanding perspectives of others, and hearing marginal voices. A proposed new image of the engineer as a “problem solver and problem definer” helps move engineering educators toward the practice of teaching problem definition in core technical courses so that a new culture can be sculpted; one that encourages political and self awareness, understanding others’ perspectives, and the ability to listen to traditionally marginalized voices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".