Experiences & Reflections on a Student Collaboration to Design a Public-Facing Digital Habitat for CEEA’s Humanities & Engineering SIG
Bibliographic record
Abstract
This paper discusses a project-based learning experience organized by members of CEEA’s Engineering and Humanities Special Interest Group (SIG). The project involved collaboration with students to design a publicly accessible digital community that would enable SIG members to gain deeper insights into the academic activities of their peers and the work being done across Canada at the intersection of engineering and humanities. The learning experience focused on three main objectives: presenting students with a real-world problem to solve, collaborating with them to explore system design ideas to enhance communication within the SIG, and nurturing student interest in the intersection of engineering and humanities. While the first two objectives were achieved, fostering students' appreciation for the humanities and engineering proved challenging, as we missed several opportunities for broader engagement. This paper will detail how we organized the learning experience, the outcomes of student learning, our reflections on the whole experience, and future work—namely, the project's next steps, and our call-to-action for a deeper conversation, and proposed grassroots activism, on the critical role of engineering educators in promoting the understanding of the significance of the humanities in engineering.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".