Bibliographic record
Abstract
Six years ago, a Seminar Series was implemented in the Engineering Strategies and Practice course for first-year engineering at the University of Toronto.The learning objectives of the seminar series are to teach students communication as an engineer, independent learning, and systems thinking.Additionally the seminar series creates a forum for faculty, and other professional mentors, to imbue a passion for learning in a more intimate environment than is normally associated with first-year courses.The interactive seminar series has proven popular with both the students and the 40+ volunteer seminar leaders recruited from academia, industry, the local community, and non-profit organizations.These seminar leaders mentor their students through a discussion of the technical, social, environmental, economic, legal, ethical, political, and/or human factors associated with an engineering-related topic of the seminar leader's choosing.The past, present, and future of the seminar series will be discussed in this paper.By measure of self-reported growth the learning objectives are being met and as such the seminar series is considered a success.It is predicted to continue, with goals of continuous improvement, for years to come.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.266 | 0.085 |
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".