Bibliographic record
Abstract
Holography is one of the most intuitive methods to teach optics, covering many concepts of introductory optics courses, in a visual manner. At the same time it provides a bridge between sciences and art. For these reasons, the Institute for Optical Sciences at the University of Toronto in collaboration with the Ontario College of Art and Design (OCAD) has started an undergraduate course on holography. This course is unique from a number of perspectives. It is a collaboration between two Toronto post secondary education institutions. Also, it enrolls both science and art students, and teaches both the artistic and scientific aspects of holography. Besides the direct learning outcome of the course material, an equally important gain is for art and science students to work together on projects, learning from each others’ strengths. The course is completely hands-on, with students given individual access to the holography studio (under the supervision of a teaching assistant) to complete the required projects in the course. The projects are complemented with lectures that cover the necessary concepts in holography, such as wave propagation, interference and diffraction. The students also receive an introduction to other uses of interference and diffraction. Since the course is taken by art as well as science students, the lectures are delivered very conceptually. Students produced some stunning holograms as part of their projects and rated the course very positively with enthusiastic reviews.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.009 |
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".