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Record W2965319379 · doi:10.36510/learnland.v12i1.975

Global Robotics Competition Meets Inclusive Education: The Exceptional Journey of Five Resilient Students

2019· article· en· W2965319379 on OpenAlexaffvenue
Simon Jameson, Sara Iatauro

Bibliographic record

VenueLEARNing Landscapes · 2019
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRoboticsCompetition (biology)Artificial intelligenceEducational roboticsRobotComputer scienceMathematics educationEngineeringPsychology

Abstract

fetched live from OpenAlex

The 2018 edition of RoboCup allowed young people from Centre de formation en entreprise et récupération (CFER) de Bellechasse to demonstrate their skills in programming and robotics. In this school, as in many schools, the use of technology is a particularly effective learning tool, especially for students facing challenges. The construction of a robotic device has allowed them to develop several skills, some of which can be reinvested in various contexts, whether at school or in their professional life. This commentary briefly presents the exceptional journey of Bellechasse students who were able to overcome many obstacles and compete with gifted students from around the world.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.371
Teacher spread0.359 · 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.

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

Citations1
Published2019
Admission routes2
Has abstractyes

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