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Benefits of transcultural fostering

2010· article· en· W1658386089 on OpenAlexaffabout
Jason Brown, Jennifer Sintzel, Natalie George, David St. Arnault

Bibliographic record

VenueChild & Family Social Work · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsWestern University
Fundersnot available
KeywordsCultural humilityHumilityPsychologyMultidimensional scalingCluster (spacecraft)Telephone surveyDevelopmental psychologySocial psychologyPedagogyCultural competencePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT Licensed Canadian foster parents residing in a central province where Aboriginal children have been overrepresented in child protection caseloads and Aboriginal adults under‐represented as caregivers were asked about their experiences fostering children from a different culture than their own during telephone interviews. In response to the question ‘What are the benefits of fostering children who have different values, beliefs and traditions than you?’, 48 unique responses were received. These responses were independently grouped together by foster parents and the groupings analysed using multidimensional scaling and cluster analysis. Six concepts emerged. They included learning about a different world view, reflecting on one's own beliefs, an opportunity to share and change, confidence to foster across cultures, humility, and seeing children teach and learn from each other. Similarities and differences between the results and existing research were identified and research implications were described.

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.008
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.029
GPT teacher head0.274
Teacher spread0.245 · 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

Citations4
Published2010
Admission routes2
Has abstractyes

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