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Record W4404115024 · doi:10.1002/car.2905

Consequences of parental neglect of academic performance Brazilian child

2024· article· en· W4404115024 on OpenAlexaff
Mara Silvia Pasian, Marina Rezende Bazon, Priscila Benítez, Carl Lacharité

Bibliographic record

VenueChild Abuse Review · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNeglectPsychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract The negative effects of child neglect on the development of children are serious and have far‐reaching negative consequences for children, especially in Brazil, with great social inequality. The research investigated the consequences of neglect for children in their early years of schooling. The academic performance was evaluated in three groups: children with formal notification of neglect, children with suspected unreported neglect and children who did not suffer any form of abuse. Information was collected from different sources: parents/caregivers, teachers and the children themselves. The groups comprised children in their early school years, aged between six and eight. The results showed that neglected children had borderline or clinical levels of school skills and below‐average school performance. The reference group had, for the most part, normal scores and average or above‐average school performance. Considering that school is a protective factor, children with learning difficulties and neglected children need support that favours child development.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.385
Teacher spread0.358 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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