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Record W4393898954 · doi:10.18260/1-2--45408

Creating Equity-Focused STEM Learning Programs with k2i academy

2024· article· en· W4393898954 on OpenAlexafffundabout
Lisa Cole, Michelle Tsui-Woods, Vanessa Ironside

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsYork University
FundersDirectorate for STEM EducationMinistère de l’Éducation, Gouvernement de l’OntarioYork University
KeywordsEquity (law)Computer scienceMathematics educationPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Abstract k2i (kindergarten to industry) academy within the Lassonde School of Engineering at York University works to meaningfully design and integrate equity and inclusion based science, technology, engineering and math (STEM) programs into all areas of education. These programs address systemic barriers that limit youth from succeeding in STEM areas, pursuing further education and finding a place in industry. The Bringing STEM to Life: Work-Integrated Learning program was designed to address inequities for underrepresented high school students by offering a high school physics credit during the summer in addition to a paid position as a Lab Assistant collaborating with Lassonde Faculty researchers and industry partners. k2i academy partners with school boards in the greater Toronto area to identify Women, Black and Indigenous students within their communities to participate in the experience. The educators, faculty researchers and undergraduate mentors who facilitate the work collaborate to create culturally relevant curriculum that focuses on engineering design, coding and computational thinking with a focus on sustainability through the use of the United Nations Sustainable Development Goals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.315
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

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