How to Win Friends and Influence Industry Collaborators
Bibliographic record
Abstract
The Capstone process helps prepare Civil Engineering students for a rapidly evolving practice now facing many urgent social, economic and environmental pressures. Recent experience in identifying suitable capstone projects and working effectively with industry collaborators and student teams will be discussed. The project portfolios will be reviewed, and the approach to recruiting and retaining collaborators, working with faculty advisors, and supporting student teams will be summarized. Lessons learned from all these perspectives provided important adjustments to the uOttawa approach, which in past semesters has succeeded in providing all students in as many as to 25 teams in a semester with an industry collaborator and a valuable opportunity to enhance their skills in communications, planning, creative engineering solutions, and interdisciplinary teamwork.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.064 |
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".