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Record W2899160616 · doi:10.1177/1476718x18809393

Using social knowledge while interacting at the classroom sand center: Facework and cohesion strategies

2018· article· en· W2899160616 on OpenAlexafffund
Shelley Stagg Peterson, Alison Altidor-Brooks

Bibliographic record

VenueJournal of Early Childhood Research · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCohesion (chemistry)PsychologyLiteracyNarrativeFlexibility (engineering)PedagogySocial psychologyMathematics educationLinguistics

Abstract

fetched live from OpenAlex

The goal of this study is to inform teachers’ practice by identifying specific language strategies that young children use in their play and suggesting ways that teachers can build on our findings to support students’ language and literacy. Deductive analyses of video-recordings of 5-year old children playing at the sand center revealed that children used cohesive strategies, such as repetitions and conjunctions, to tie together the ideas from one speaker to the next, in order to maintain the flow of the play. Children also used facework strategies, such as complimenting peers, softening regulatory language with words such as “just,” and showing interest in others’ activities. These strategies helped children to build relationships with peers and enhance their positive self-esteem as members of the play group. Children used language primarily for imaginative purposes, in addition to communicating information, regulating others’ behavior, and expressing their individuality and emotional responses to activities at the sand center. Primary teachers may find these results useful for guiding assessment of children’s knowledge of and flexibility in using linguistic and semiotic resources to achieve social purposes and to create cohesive narratives in informal interactions.

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 categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.198
GPT teacher head0.422
Teacher spread0.225 · 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 designQualitative
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

Citations4
Published2018
Admission routes2
Has abstractyes

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