The Optical Terrace: an example of multidisciplinarity in student-led initiatives
Bibliographic record
Abstract
In honor of UNESCO’S First International Day of Light (IDL) in 2018, Universit´e Laval’s SPIE Student Chapter set out to design a large-scale outreach initiative that would be both artistic and educational. The goal of this project was to design a new way for people to interact with light through human-scaled experiments outside of traditional channels such as schools, museum or libraries. It was realized that the required skills to fulfill requirements would exceed those of the mostly physics-oriented student chapter. Between the three consecutive yearly editions of The Optical Terrace, multidisciplinarity was a focus for this student-led initiative. Promoting collaboration between physics, architecture, art, marketing and communication has proven to be a challenge that our team has learned to manage. In this paper, we will explain the solutions we came up with that had the most success in keeping active involvement of our members and to steer the design within our requirements.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".