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Record W4285798484 · doi:10.37291/2717638x.202232170

Agency as assemblage: Using childhood artefacts and memories to examine children’s relations with schooling

2022· article· en· W4285798484 on OpenAlexafffundabout
Julie C. Garlen, Debbie Sonu, Lisa Farley, Sandra Chang‐Kredl

Bibliographic record

VenueJournal of Childhood Education & Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsConcordia UniversityYork UniversityCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAgency (philosophy)Early childhood educationAssemblage (archaeology)Meaning (existential)Childhood studiesStructure and agencyPencil (optics)Object (grammar)Early childhoodPsychologySociologyPedagogyDevelopmental psychologySocial scienceHistoryArchaeologyEngineeringLinguistics

Abstract

fetched live from OpenAlex

In this article, we explore how childhood artefacts and memories might help us think retrospectively about children’s agency and its relationship to schooling and teaching. Across four university sites in Canada and the United States, we asked undergraduate students in teacher education and childhood studies programs to choose an artefact or object that encapsulates contemporary conceptions of childhood and to discuss them in a focus group setting at each site. Building on three participants’ descriptions of how they remembered and reflected upon school-oriented objects – a progress report, a notebook, and a pencil sharpener – we explore how participants used their artefacts in ways that allow us to theorize children’s agencies as assemblages, where agency is relational and contingent on multiple social and cultural factors. Drawing on our participants’ interpretations, we consider how a reconceptualized concept of agency may expand our understanding of the possibilities of children’s agencies in school and raise new questions about the meaning of childhood within contexts of teacher education and childhood studies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.304
Teacher spread0.287 · 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

Citations8
Published2022
Admission routes3
Has abstractyes

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