The Montréal photonics networking event: lessons learned from online and in-person collaborative environments for graduate researchers
Bibliographic record
Abstract
Montréal is home to over 100 companies and six universities with photonics activities that drive regional economic development. This paper provides an update on the Montréal Photonics Networking Event, an annual meeting heading into its’ 8th edition in 2024. The event’s stated mission is to build a collaborative environment for the development of student researchers, with the ambition to facilitate research synergies and connect with the industry to showcase research and career opportunities. Since 2015, online and in-person events have taken place, with a measured growth of 50% year-on-year, a cumulative reach of 600 participants, and the establishment of 30 partnerships with industry, photonics student chapters, research clusters, and socio-economic development partners. The event is coordinated by a network of volunteer students and professional's representative of the attendees and collaborators. Activities, promotional material, and marketing strategies to create audience engagement before, during, and after the event will be presented, along with lessons learned to enable peer-to-peer development online and in person across multiple academic research institutions.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".