Are there curricular differences between biology-based and application-based "bio" engineering disciplines?
Bibliographic record
Abstract
Several authors have previously promoted the transformation of the application-based agricultural engineering discipline into a biology-based biological engineering discipline. A systematic analysis of titles for courses being taught by ASABE-umbrella programs across North America was undertaken to identify curricular differences between biology-based and application-based “bio” engineering disciplines. Based on 44 ASABE-umbrella programs analyzed, the four most commonly used program names were biological engineering (25%), biosystems engineering (20%), biological systems engineering (15.9%) and agricultural engineering (13.6%). Definitions of these four program names were reviewed; biosystems, biological systems and agricultural engineering are typically defined such that they are best described as application-based “bio” engineering disciplines while biological engineering is best described as a biology-based engineering discipline. Based on statistical analysis of the frequency of words in course titles, there was a significant increase in the usage of the word “food” and a lack of the word “project” in the course titles within biological engineering programs. Over half of the unique options were found in biological engineering programs suggesting that they do offer unique course content compared with biosystems, biological systems and agricultural engineering degree programs, however, it is noteworthy that four options appear across all four degrees. It is concluded that there are curricular differences between biology-based and application-based “bio” engineering disciplines, however, the curricular differences are not as substantive as one might conclude from the philosophical discussions in the literature. Alternatively, it may simply not be possible to detect curricular differences solely from an analysis of the course titles
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".