Towards a Transformative Collaboration: Technical Writing, Engineering, Industry
Bibliographic record
Abstract
This paper reports on a work in progress collaboration between Engineering and English faculty at an American university in the Middle East region and examines the initial impact of the team's reorganization of a required Technical Professional Writing course on engineering students' educational experience as they learn effective and relevant professional communication skills in the field of engineering; as they network with mentors from various industries; and as they train to be effective writers and competitive candidates in their engineering fields.We hypothesize that the significant collaboration between English and Engineering faculty in developing assignments, providing feedback to students throughout their projects, and assessing students' final products, as well as the partnership with various partner industries, considerably improves our students' writing journey at TAMUQ as they learn effective and relevant professional communication skills in the field of engineering.We also discuss the steps forward to make this collaboration a model for other courses in our curriculum at our institution.
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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 | 0.055 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.027 | 0.011 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".