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Record W7132993662

Children Do Science: Learning to Recognize Children’s Funds of Science Knowledge Expressed in a Community-based Research Club Initiative

2022· dissertation· W7132993662 on OpenAlexaffabout
Kristen Drumheller Schaffer

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsClubAffordanceNarrativeExpansiveScience educationSociology of scientific knowledgeSocial science educationCommunity of practice
DOInot available

Abstract

fetched live from OpenAlex

Children do science. Yet, children from lower-income communities are less likely to be recognized for doing science. This deficit narrative bolsters the construction of systemic obstacles which complicate how educators recognize and mobilize children’s science (Carlone et al., 2014). This dissertation explores how science is socially and culturally constructed via school curriculum, the academy, and public science forums, such that they generate and gatekeep a world of science where there are insiders and outsiders (Dawson, 2014b). In collaboration with a community science club provider and an urban primary school in Central Canada, a new experimental after school science club was created. Twenty children aged 8 to 11 were joined by two staff members and me as a volunteer club facilitator, as we explored science concepts and worked towards a showcase presented to the school community. Using critical practitioner research (Cochran-Smith & Lytle, 2009), I explored the affordances and challenges of supporting children’s funds of knowledge (FoK) (Moll et al., 1992), specifically their funds of science knowledge (FoSK) as expressed in the space of the club. I share four stories which weave together the data, my critical reflections, and relevant literature: 1) Coming to terms with STEM; 2) Robot FoSK—Child engineers and social innovators; 3) Slime and the scientific expression of children; and 4) The science centre—It belongs to us. The narrative analysis demonstrates the how children do science in a manner which aligns with the common characteristics of science, and the more expansive and creative qualities of children’s science. I make suggestions for how a FoSK framework can support educators to recognize and mobilize children’s science skills and knowledge as they learn. Further recommendations highlight the necessity of supporting children’s science talk, play, and creativity; the equitable potential of community-based informal science education; and the importance of interrogating one’s own power and capacity for recognizing FoSK. In sharing my experience, I hope to motivate researchers and educators to look inward and challenge their assumptions, personal and institutionalized, and adapt their practices to better support children as they do science.

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)0.067
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0670.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.043
Science and technology studies0.0200.016
Scholarly communication0.0010.001
Open science0.0080.001
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.232
GPT teacher head0.552
Teacher spread0.320 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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
Published2022
Admission routes2
Has abstractyes

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