UNIVERSITY AND COMMUNITY COLLEGE STUDENTS CAN SHARE A LEARNING EXPERIENCE
Bibliographic record
Abstract
This paper describes a pilot collaboration between Queen’s University and Sir Sandford Fleming College of Applied Arts and Technology in Peterborough Ontario. Since 1994 Queen’s has offered projects where students learn by solving problems for fee paying industrial clients. Known as Technology Engineering and Management (TEAM) student participants form multidisciplinary teams to consult for business clients. In addition to engineering students, commerce and arts students have often participated in the teams. In the Applied Projects program at Fleming College, third year engineering technology student teams solve problems for enterprise sponsors. A pilot group of engineering technology students from Fleming College worked with students in two Queen’s University TEAM projects. In industrial practice, engineers and engineering technologists often collaborate on solving problems. This collaboration rarely occurs in an educational setting. In the 2002-2003 academic year the pilot exercise simulated the professional working relationship between engineers and technologists. This paper gives a description of the experience and the motivation to undertake this unique collaboration. The most important aspect of the presentation is a critical assessment of the University/College collaboration -- what worked, what problems arose, and what improvements are suggested.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.015 |
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".