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Record W2258970104 · doi:10.1177/2043610615597139

‘Be careful!’ Using our words as a discursive exploration of early childhood educators regulating children’s play

2015· article· en· W2258970104 on OpenAlexaffabout
Noah Kenneally

Bibliographic record

VenueGlobal Studies of Childhood · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsGovernmentalitySocializationPerspective (graphical)DisciplineSociologyEarly childhoodEarly childhood educationSubject (documents)Space (punctuation)PsychologyDiscourse analysisPedagogyDevelopmental psychologySocial sciencePoliticsLinguistics

Abstract

fetched live from OpenAlex

What early childhood educators say to children on playgrounds shapes children’s behaviour. ‘Be careful!’ may be the most common phrase uttered on playgrounds across Ontario, as early childhood educators manage children’s play and work to maintain their safety. These utterances, and the disciplinary practices that accompany them, shape what is acceptable and unacceptable for children to do and, ultimately, what kind of children they can be. Making use of analytic strategies derived from Foucault, I take some first steps to show that injunctions to ‘be careful’ and other similar utterances regulate children’s behaviour to produce a particular child-subject, while in the same moment revealing much about some of the discourses at work in the playgrounds of many early learning settings. I propose that these discourses – the discourses of safety, socialization and purposeful play, all embedded within an overarching developmental discursive framework – connect early childhood educators’ utterances and practices on playgrounds to concepts of discipline and governmentality. I also explore in this article how a Foucauldian perspective may provide educators a space to question established ideas regarding children and their play and explore new approaches for ensuring children’s safety without controlling them.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.082
GPT teacher head0.379
Teacher spread0.297 · 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 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

Citations0
Published2015
Admission routes2
Has abstractyes

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