The Honours B.Sc. degree program in photonics at Wilfrid Laurier University
Bibliographic record
Abstract
The fast growth of the photonics industry in the past several years has lead to great demand on professionals in the field of optical science and engineering. In response to this market demand, many universities have enhanced, to a certain degree, their photonics related programs in an effort to produce graduates with some degree of knowledge of photonics. A few universities in the US and Europe have gone even further creating new program specifically in photonics/optical science or engineering. However, we have not seen this type of programs being created in Canada so far. At Wilfrid Laurier University (WLU), we have conducted research on the photonics related programs in Canada, USA, and Europe. We have found that the Department of Physics and Computing at WLU has a unique position and a great opportunity to create the first Canadian Honours B.Sc. Degree Program in Photonics Science. We feel that dual nature of physics and computing of our Department offers the best combination for such a program. In this paper, we discuss the following items: (1) The curriculum of the Honour B.Sc. degree program at WLU. (2) Is a degree in photonics too narrow? (3) Issues related to enrolment, graduates, and potential jobs opportunities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.024 |
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".