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Record W4401344039 · doi:10.1177/20436106241267834

Reconceptualizing early childhood education: Cartographic relational stories

2024· article· en· W4401344039 on OpenAlexaff
Fikile Nxumalo, Joanne Peers

Bibliographic record

VenueGlobal Studies of Childhood · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDialogicSociologyTemporalitiesRelation (database)EthosSituatedEarly childhood educationAffordanceModalitiesEpistemologyDeleuze and GuattariPedagogyAestheticsSocial sciencePsychologyPolitical scienceArt

Abstract

fetched live from OpenAlex

In this article, we enact a partial cartographic storying of reconceptualist turns in our work. We do this by situating ourselves in relation to each other and our work across time as a mode of tracing the (situated) possibilities that these turns have enacted for children-in-relation with worlds. In enacting this dialogic and cartographic storying, we collaborate in a way that is inspired by Black methodologies. This means that we intentionally think with liberatory possibilities in our work in early childhood education research and practice across modalities, disciplines, temporalities and geographies. Importantly, like Black methodologies this co-theorizing is also ontological; it is inseparable from our own relational becomings. Following an anticolonial ethos, we are interested in the liberatory potentials of our work, at multiple scales and in different but specific places. We attempt to enact the difficult task of (re)storying our childhood education research and practice in ways that pay attention to the interconnected presences and effects of white supremacy, human supremacy and colonialism, while simultaneously refusing to reinscribe a flattened damage-centred understanding of children, educators and their relational worlds.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.045
GPT teacher head0.354
Teacher spread0.309 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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