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Record W2111840975 · doi:10.1093/heapro/dai017

Welfare babies: poor children's experiences informing healthy peer relationships in Canada

2005· article· en· W2111840975 on OpenAlexaffabout
Lynne Robinson, Lynn McIntyre, Suzanne Officer

Bibliographic record

VenueHealth Promotion International · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPovertyEmbarrassmentFeelingPsychologyDevelopmental psychologyMental healthSocial psychologyQualitative researchSociologyEconomic growthPsychiatry

Abstract

fetched live from OpenAlex

Positive peer relationships among children living in poverty are important for their well-being, resiliency and mental and physical health. This paper explicates the 'felt experience' of children living in poverty, and the implications of these experiences for healthy peer relationships, from a re-analysis of two qualitative research studies in Canada examining children living in food insecure circumstances. Poor children feel deprived, part of the 'poor group', embarrassed, hurt, picked on, inadequate and responsible. Poor children internalize their own lack of social resources in feelings of deprivation. They experience negative feelings relative to their peers-inadequacy, embarrassment and hurt. Children do identify group membership but it is not used as a social resource, as it could be, but rather as a symbol of social segregation. Children also feel responsible for ameliorating some of the effects of their poverty and this seems to strengthen their relationship with their mothers. This could equally be translated into peer-related support, such as standing up to poor bashing, or engaging constructively with higher social class peers. Health promotion strategies that seek to foster positive peer relationships and enhance children's sense of belonging should offer novel social environments in which poor children can engage a variety of peers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.405
Teacher spread0.339 · 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.

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

Citations15
Published2005
Admission routes2
Has abstractyes

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