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Record W2912324750 · doi:10.20360/langandlit29369

Hidden Children: Using Children's Literature to Develop Understanding and Empathy Toward Children of Incarcerated Parents

2019· article· en· W2912324750 on OpenAlexaffvenueabout
Val Plett Reimer

Bibliographic record

VenueLanguage and Literacy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEmpathyPsychologyLiteracyDevelopmental psychologyPicture booksSocial psychologyPedagogy

Abstract

fetched live from OpenAlex

Abstract Research indicates that children whose parents are incarcerated are a vulnerable group of people with poor life outcomes. Yet these children are not tracked in the Canadian system, making it difficult for schools to respond with appropriate supports. How can schools be inclusive to this hidden demographic of children? Framed in theories of Critical Literacy and Ethic of Care, the author proposes the use of story to develop understanding and empathy. Research shows that acknowledging these children’s experiences through story helps them to feel validated while broadening capacity for empathy among other children. Can a story develop empathy toward children of incarcerated parents? To answer this question, the author wrote a picture book about a child who visits her mother in jail, and read the story to three groups of children, interspersed and followed by rich discussions. The story elicited empathetic responses from all students, suggesting the benefits of this approach.

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)0.004
metaresearch head score (Gemma)0.013
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.005
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.296
Teacher spread0.280 · 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

Citations6
Published2019
Admission routes3
Has abstractyes

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