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Record W2979818285 · doi:10.29173/iasl7372

School library as the active learning center of the school

2019· article· en· W2979818285 on OpenAlexvenueno aff
Antonija Lujanac

Bibliographic record

VenueIASL Annual Conference Proceedings · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Training Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityPsychologyPhysical activityLife styleStyle (visual arts)Medical educationActive learning (machine learning)Mathematics educationMental healthPedagogyApplied psychologyMedicineComputer scienceSocial psychologyPhysical therapyVisual arts

Abstract

fetched live from OpenAlex

The fact is that many children, especially teenagers, suffer from mental and emotional stress and problems with their learning skills. Many of them are at risk of cardiovascular and locomotor system diseases because of the sedentary life-style. Most health experts agree that kids live sedentary if they do not exercise at least 30 minutes three times a week, have a lessons that requires little physical activity and sit most of the time. Physically active kids feel better what is prerequisite for successful learning but it is very often that pupils at school sit and learn using tablets, books, smartphones and computers. The needs of today pupils means active learning but also active way of living. School library is one of the most appropriate location in the school to connect movement, creativity and learning.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.232
Threshold uncertainty score0.776

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0070.001
Scholarly communication0.0110.006
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2320.112

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 head0.035
GPT teacher head0.368
Teacher spread0.333 · 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 designNot applicable
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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