An experiment in integrating communication into the biosystems engineering design trilogy
Bibliographic record
Abstract
Technical communication is recognized as a fundamental and necessary skill for engineers of all disciplines in all types of careers. As aresult, it is firmly entrenched as a mandatory course in Canadian faculties of engineering. Technical communication, however, has notalways been palatable for engineering students because it emphasizes the `softer' skills which students tend to devalue in comparison tothe seemingly more objective, `harder' engineering skills. Engineering faculties generally offer technical communications as a separatedistinct course. This results in students viewing these courses as separate from the engineering curriculum and placing them in with theirother elective obligations. Opportunities for innovative ideas to stress the importance of technical communications while incorporatingthis skill set into the engineering students' toolbox are being welcomed. The Department of Biosystems Engineering at the University ofManitoba has integrated technical communication instruction into a capstone design course. A trained technical communication expertteaches the course together with professional engineers. Because the technical communication instruction relates to the design projectsbeing completed, the students see the value of technical communication in the overall design process; thus it is not regarded as aseparate or unrelated activity.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".