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Record W2511720136 · doi:10.29173/irie144

Robotics and Development of Intellectual Abilities in Children

2006· article· en· W2511720136 on OpenAlexvenueno aff
Miguel Ángel Pérez Álvarez

Bibliographic record

VenueThe International Review of Information Ethics · 2006
Typearticle
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual developmentRoboticsRobotArtificial intelligencePsychologyCognitive scienceComputer scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

It is necessary to transform the educative experiences into the classrooms so that they favor the development of intellectual abilities of children and teenagers. We must take advantage of the new opportunities that offer information technologies to organize learning environments which they favor those experiences. We considered that to arm and to program robots, of the type of LEGO Mind Storms or the so called “crickets”, developed by M. Resnik from MIT, like means so that they children them and young people live experiences that favor the development of their intellectual abilities, is a powerful alternative to the traditional educative systems. They are these three tasks those that require a reflective work from pedagogy and epistemology urgently. Robotics could become in the proper instrument for the development of intelligence because it works like a mirror for the intellectual processes of each individual, its abilities like epistemologist and, therefore, is useful to favor those processes in the classroom.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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