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Record W2949068824

‘I get to learn more stuff’ : children’s understanding of wellbeing at school in Winnipeg, Manitoba, Canada

2019· article· en· W2949068824 on OpenAlexaboutno aff
Elizabeth Huynh

Bibliographic record

VenueOAR@UM (University of Malta) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyPsychologyLibrary scienceMedicineComputer science
DOInot available

Abstract

fetched live from OpenAlex

This purpose of this study was to explore how school-aged children understand dimensions of wellbeing in a Canadian context in participation of the Multi-national Qualitative Study – Children’s Understanding of Wellbeing. Twenty-one school-aged children (boys = 8, girls = 13) participated in semi-instructed interviews facilitated by tactile, tasked oriented interview tool. Participants were recruited from seven before- and-after-school child-care programs throughout the city of Winnipeg, Manitoba, Canada. The wellbeing of children at school was influenced by the quality of the relationships they had with their teachers. Children recognized teachers as being essential agents in their learning process and teaching them essential skills for their future. Children who described feeling positive about school were children who felt that their teachers were supportive, provided creative ways to learn, and listened to their ideas and concerns. Conversely, children who described negative feelings about school discussed experiencing teachers who did not value their ideas and concerns, and were not supportive in their individual needs as a learner. The teaching style of teachers affect children’s wellbeing at school. Teachers who promote wellbeing at school and positive feeling associated with learning are those who consider the voices and needs of their students, as well as make efforts to incorporate those considerations in their curriculum.

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 score0.221
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 head0.016
GPT teacher head0.217
Teacher spread0.201 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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