k2i academy: An Innovative Ecosystem Addressing System Barriers in STEM from Kindergarten to Industry
Bibliographic record
Abstract
k2i (kindergarten to industry) academy within the Lassonde School of Engineering at York University is an innovative ecosystem that works to meaningfully design and integrate equity and inclusion based STEM programs that address persistent problems in education. In order to address these barriers, k2i academy has developed an Inclusive Design Framework that guides our work and ensures that our programs are designed with equity, diversity and inclusion as the central principle. This framework was implemented in the Bringing STEM to Life: Work Integrated Learning program to address inequities for underrepresented high school students. The program participants earned a high school physics credit during the summer while gaining employment experience as a Lab Assistant working on projects with mentors. Through collaborations with Lassonde Faculty researchers, industry partners, and educational leaders in school boards, the program identified that these experiences allowed youth and K-12 educators to broaden their understanding of STEM, developed critical technical and professional skills and enabled youth to imagine and see themselves in a STEM career.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".