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Record W2140835647 · doi:10.1080/10304312.2015.1025367

<i>Coraline</i> 's split mothers: the maternal abject and the childcare educator

2015· article· en· W2140835647 on OpenAlexaffabout
Sandra Chang‐Kredl

Bibliographic record

VenueContinuum · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsConcordia University
Fundersnot available
KeywordsContext (archaeology)Early childhood educationRepresentation (politics)Argument (complex analysis)Social workCreedSociologyGender studiesPsychologyGovernment (linguistics)Psychoanalytic theoryEarly childhoodDevelopmental psychologyPedagogyPsychoanalysisPolitical scienceMedicineHistory

Abstract

fetched live from OpenAlex

Early childhood education and care is, again, a focus of debate in Quebec, Canada. Government-subsidized childcare programs are being cut and the province's plan to open prekindergartens for children in impoverished areas is being met with contention. Invariably, the focus of the debate is on the children's needs, the parents’ needs and society's needs. The educator is rarely mentioned. In this paper, I focus on the early childhood educator's subjective experiences (Chang-Kredl 2013) in a social system that undervalues their work as maternal, endorsing Grumet's (1988) close attention to women's internal experiences as a means of generating social change in education. I compare the social positioning of early childhood educators, in a Canadian context, with the representation of abjected maternal figures in a children's film, namely the split mothers in Coraline (2009). The argument for such a comparison is made through theories of maternal thinking (Ruddick 1995; Mullin 2009) and feminist readings of psychoanalytic theories related to the abject and the monstrous-feminine (Kristeva 1982; Creed 1993).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.407
Threshold uncertainty score0.809

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.014
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.294
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2015
Admission routes2
Has abstractyes

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