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Record W4253138819 · doi:10.32920/ryerson.14649504

Team Beam: Economizing Children’s Bee-Havioral Development

2021· preprint· en· W4253138819 on OpenAlexaffabout
Michael Conley

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicArchitecture, Design, and Social History
Canadian institutionsProfessional Engineers OntarioWestern University
Fundersnot available
KeywordsAgency (philosophy)IncentiveWork (physics)PsychologyProcess (computing)Personal developmentApplied psychologyPublic relationsKnowledge managementMarketingEngineeringSociologyComputer scienceBusinessSocial sciencePolitical science

Abstract

fetched live from OpenAlex

This research paper uses behavioral economics to design a children’s play installation that facilitates soft skill development. By reviewing existing literature from Education, Early Development and Behavioral Economics, my installation supports improvements in collaborative behaviours within child-child and parent-child relationships. Literature across research disciplines explains how early soft skill achievement influences life course outcomes at school, work, home and in personal relationships. The installation’s user difficulty, material composition, colouring and incentives nudge children aged 2–4 toward making emotionally beneficial decisions. Because it is designed for a museum setting, the assumed expectations of installation users, other visitors and the host museum are acknowledged in the design. The installation’s assumed effectiveness, specific location and accessibility features are produced from personal work experiences at the Royal Ontario Museum, interviews with field professionals, attention to parents’ agency, existing literature focused on inclusive, play-based spaces and an iterative creative process based on design thinking methodology.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.037
GPT teacher head0.224
Teacher spread0.187 · 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 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
Published2021
Admission routes2
Has abstractyes

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