Universalized Narratives: Patterns in How Faculty Members Define “Engineering”
Bibliographic record
Abstract
B ackground U.S. engineering educators are discussing how we define engineering to ourselves and to others, such as in the recently released U.S. National Academy of Engineering (NAE) report, Changing the Conversation . In these conversations, leaders have proposed the skills, knowledge, processes, values, and attitudes that should define engineering. However, little attention has been paid to the daily work of engineering faculty, through their engineering research and teaching students to be new engineers, that puts these discipline‐defining ideas into practice in academia. P urpose (H ypothesis ) The different types of narratives engineering faculty explicitly or implicitly use to describe engineering are categorized. Categorizing these common narratives can help inform the nationwide conversation about whether these are the best narratives to tell in order to attract a diverse population of future engineers. D esign /M ethod Interviews with ten engineering faculty at a research‐extensive university were conducted. Interview transcripts were coded thematically through coarse then fine coding passes. The coarse codes were drawn from boundary theory; the fine codes emerged from the data. R esults Faculty members' descriptions moved within and among the narratives of engineering as applied science and math, as problem‐solving, and as making things. The narratives are termed “universalized” because of their broad‐sweeping discursive application within and across participants' interviews. C onclusions These narratives drawn from academic engineers' practice put engineering at odds with recommendations from the NAE report. However, naming the narratives helps make them visible so we may then develop and practice telling contrasting narratives to future and current engineering students.
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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".