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Record W4389954386 · doi:10.21810/sfuer.v15i1.6159

Science in Informal Learning Spaces: Tinkering Space at Science World

2023· article· en· W4389954386 on OpenAlexaffvenueabout
K. J. Lee

Bibliographic record

VenueSFU Educational Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsScience World at Telus World of Science
Fundersnot available
KeywordsSpace (punctuation)Informal learningSpace ScienceScience learningData scienceScience educationComputer scienceSociologyPsychologyMathematics educationEngineeringPedagogy

Abstract

fetched live from OpenAlex

Aside from unique architecture Science World has become iconic for families in Vancouver to explore hands-on exhibits and galleries that nurture their process of discovery and inspire connection with their natural, physical, and built environments. Our value of inquiry-rich, play-based, cross-disciplinary learning is embedded in every aspect of design, from fun interactive exhibits, engaging stage shows, and unique school programs. Throughout Science World you will discover that each gallery focuses on different themes and topics. Gallery spaces have their own narratives, learning goals, and outcomes. One of newest galleries at Science World is our Tinkering Space: The WorkSafeBC Gallery. The Tinkering Space has daily tinkering programming where you can solve problems, make new things from existing parts, create something cool and imaginative, and learn through experimenting and making mistakes. You’ll also learn about the science behind safety and how important it is to choose the right tools for the job. This informal learning environment captures the playful spirit of Science World all in one place. Creating the Tinkering Space is an iterative journey, and we continue to work hard to build out the visitor experience and pedagogical practice we have today.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.419
Teacher spread0.377 · 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 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
Published2023
Admission routes3
Has abstractyes

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