Promoting Open-Science and Accessible Student Training: The Open-Science fabrication laboratory model
Bibliographic record
Abstract
The Open-Science fabrication laboratory movement is an ongoing student initiative at the School of Optometry of the University of Montreal. The pilot project, the OptoFAB, is a collaborative space specially dedicated to producing low-cost material for research purposes based on the Open-Science and Maker movements. It aims to:1.Promote Open-Science and the fast dissemination and replication of research.2.Provide accessible training for students in a multitude of disciplines (i.e., 3D printing, 3D modeling, coding, robotics).3.Encourage interdisciplinary collaborations between the different programs (e.g., research, clinical, engineering).Our laboratory model will benefit laboratories and provide students with more opportunities during and beyond their (under)graduate studies. As students are the next generation of researchers, the Open-Science fabrication laboratory aims to give them more resources to develop multi-faceted skills, improve their self-efficacy and help them build their research careers. This paper describes our model and how we aim to measure our impact ongoingly and to improve the tools and services we put at students’ disposition. We hope that, through this paper, other universities might join the movement and replicate our model within their research programs.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".