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Record W1994446026 · doi:10.1080/02635140120046268

Children's Ideas about Strengthening Structures

2001· article· en· W1994446026 on OpenAlexafffund
Brenda J. Gustafson, Patricia M. Rowell, Dawn P. Rose

Bibliographic record

VenueResearch in Science & Technological Education · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsAlberta Advanced EducationUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVariety (cybernetics)VocabularyPedagogyMathematics educationPsychologyWork (physics)SociologyEngineeringComputer science

Abstract

fetched live from OpenAlex

This research focused on 181 school children's (aged 5-13 years) responses to an Awareness of Technology Survey question intended to explore their conceptual knowledge of structural strength. The survey responses showed that the children had many productive ideas about structural strength prior to formal classroom instruction. After instruction, the children's ideas showed little overall change. The Discussion explores how the framework of the programme and professional development opportunities did not help the teachers to implement the programme. Suggestions for productive classroom experiences related to structural strength include: introducing children to a greater variety of building materials; assisting children to explore the physical and mechanical properties of materials; practising with vocabulary needed to describe properties of materials; providing opportunities to understand the pushes and pulls at work in a structure; and promoting design technology discourse.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.005
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.155
GPT teacher head0.532
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2001
Admission routes2
Has abstractyes

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