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Record W2517864406 · doi:10.1080/09575146.2016.1219844

Children’s rough and tumble play: perspectives of teachers in northern Canadian Indigenous communities

2016· article· en· W2517864406 on OpenAlexafffundabout
Shelley Stagg Peterson, Audrey Madsen, Jayson San Miguel, Soon Young Jang

Bibliographic record

VenueEarly Years Journal of International Research and Development · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMainstreamIndigenousPerceptionNegotiationPedagogySociocultural evolutionContext (archaeology)Professional developmentFocus groupPsychologyMathematics educationSociologySocial sciencePolitical scienceGeography

Abstract

fetched live from OpenAlex

Ten teachers in kindergarten and grade one classrooms in remote northern Canadian Ojibway communities, and two consultants from a First Nations Student Success Program participated in focus group discussions about the place of rough and tumble and superhero play, and teachers’ roles in preventing relational and physically aggressive play in school. This paper reports on issues related to sociocultural influences on perceptions of play involving objects to which Indigenous children assign implicit roles as guns, and teachers’ concerns about external perceptions of teachers’ roles vis-à-vis rough and tumble play in school. Implications for teacher practice and for teacher education include establishing boundaries and negotiating rules and consequences with students, and teaching problem-solving approaches. Teachers’ expressed need for exposure to research on rough and tumble play in teacher education and professional development initiatives is consistent with the findings of previous research. This study provides perspectives from teachers in a non-mainstream teaching context on a controversial topic with mixed views coming from researchers and media reports.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.342
Teacher spread0.305 · 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.

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

Citations19
Published2016
Admission routes3
Has abstractyes

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