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Record W4308912279 · doi:10.24908/pceea.vi.15876

k2i academy: An Innovative Ecosystem Addressing System Barriers in STEM from Kindergarten to Industry

2022· article· en· W4308912279 on OpenAlexafffundvenue
Lisa Cole, Jane Goodyer, Vanessa Ironside

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsYork University
FundersDirectorate for STEM EducationMinistère de l’Éducation, Gouvernement de l’OntarioYork University
KeywordsInclusion (mineral)Equity (law)Work (physics)Diversity (politics)Underrepresented MinorityMedical educationProfessional developmentPedagogyEngineering ethicsEngineeringEngineering managementPsychologyPublic relationsSociologyPolitical scienceMedicineMechanical engineering

Abstract

fetched live from OpenAlex

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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 designObservational
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

Citations5
Published2022
Admission routes3
Has abstractyes

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