On Designing A 3d Imaging Summer Project For Ontario’s High School Students During Covid-19 Pandemic
Bibliographic record
Abstract
During the Covid-19 pandemic, like the vast majority of countries in the world, Canada was under government-mandated lockdown, creating unprecedented challenges for the higher education system. This has exacerbated the problem of gender and ethnic inequalities in the STEM field due to the sudden disappearance of in-person communication and communities that had supported minority groups. To provide emergency support and reduce the known gender / ethnic gap, at York University in Toronto we designed a 3D imaging project for Ontario’s high school (HS) students, as part of an annual summer outreach program in the Lassonde School of Engineering. The project aims to create an equitable opportunity for HS students, providing a comprehensive introduction to image processing through experiential learning. We document our design methodology and experiences in the project, as well as feedback and evaluations from participants at all levels. We believe such documentation is valuable to promote gender- and ethnic-balanced education in image processing and the broader STEM field in the future, in an increasingly unpredictable environment due to climate change.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".