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Record W4213287719 · doi:10.1177/14647001211062746

Emotional Justice as an antidote to loneliness: children's books, listening and connection

2022· article· en· W4213287719 on OpenAlexaff
Shoshana Magnet, Catherine-Laura Dunnington

Bibliographic record

VenueFeminist Theory · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsWomen's and Gender Studies et Recherches FéministesUniversity of Ottawa
Fundersnot available
KeywordsLonelinessOppressionFeelingActive listeningEconomic JusticeIsolation (microbiology)PsychologySociologyGender studiesPsychoanalysisSocial psychologyPsychotherapistLawPolitical science

Abstract

fetched live from OpenAlex

Loneliness is intimately related to the ongoing epidemics of systemic forms of oppression, including white supremacy, capitalism, heteropatriarchy and settler colonialism. The epidemic of loneliness has only intensified and grown during the isolation engendered by the COVID-19 pandemic. In this article, we aim to think about how children's picturebooks wrestle with explaining loneliness and its antidotes (connection, community) and how these picturebooks are themselves manifestations of ongoing conversations related to Emotional Justice. We conclude by reviewing a number of children's books in order to think about how the picturebook might itself be an artifact that helps to fight feelings of loneliness as well as teaching children and adults alike about the importance of connection.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.011
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.304
Teacher spread0.290 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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