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Record W2908938410 · doi:10.24908/pceea.v0i0.13105

Knowledge Mobilization Intermediaries in STEM: The roles and functions of K-12 STEM Outreach Organizations at Canadian Universities

2018· article· en· W2908938410 on OpenAlexafffundvenueabout
Scott Compeau

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsQueen's University
FundersUniversity of British ColumbiaQueen's University
KeywordsOutreachIntermediaryRelevance (law)TypologyPublic relationsKnowledge managementSociologyPolitical scienceBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

Knowledge Mobilization (KMb) is the reciprocal and complementary flow and uptake of research knowledge between knowledge producers, knowledge intermediaries, and knowledge users. The purpose of this investigation is to explore the typology of Canadian university-based Science Technology Engineering and Mathematics outreach organizations and understand if/how they function as knowledge mobilization intermediaries. Three research questions guide this first study; 1) What are the organizational features of K-12 STEM outreach organizations; 2) To what extent do STEM outreach organizations interact with K-12 educators or administration and 3) What knowledge mobilizations processes do they currently use? The methodology used for data collection will be an online questionnaire consisting of qualitative based open-ended questions. The educational importance of this study aligns with the goals of KMb as it has relevance to both within academia and beyond. Within academia, the results will contribute towards the body of knowledge within K-12 STEM education. Beyond academia, this study has value in practice, as the results will engage STEM outreach organizations in conversation about KMb strategies

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 score0.723
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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

Citations0
Published2018
Admission routes4
Has abstractyes

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