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Record W2125790181 · doi:10.1080/03124070903281135

The Travelling Idea of Looking After Children: Conditions for moulding a systematic approach in child welfare into three national contexts—Australia, Canada and Sweden

2009· article· en· W2125790181 on OpenAlexaboutno aff
Lennart Nygren, Ulf Hyvönen, Evelyn Khoo

Bibliographic record

VenueAustralian Social Work · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsnot available
FundersVetenskapsrådetForskningsrådet för Arbetsliv och Socialvetenskap
KeywordsNormativeWelfareSocial workContext (archaeology)Social WelfareSociologyPublic relationsPsychologyPolitical scienceEconomic growthEconomicsLawGeography

Abstract

fetched live from OpenAlex

Abstract Looking After Children (LAC) is an approach (care philosophy and working tools) used to assess the psycho-social development of children being cared for by child welfare agencies. It is an international initiative that was developed in England and then travelled and was translated into other contexts, most notably Australia, Canada and Sweden. This paper presents findings from an open-ended question in a survey distributed to social workers and managers using LAC and “cousin” systems in these countries. We asked respondents what advice they would give to others considering implementing these systems. Our qualitative content analyses showed that, regardless of the context, the 257 respondents gave voice to programmatic/normative arguments, reflecting mainly positive attitudes to the systems. However, managers and social workers voiced different arguments in their favour. Managers voiced normative arguments favouring the underlying principles, whereas social workers from all three countries identified the dual needs to remain flexible and to recognise the limitations of the systems, especially at the operational level. Results offer insights into approaches to change management in different contexts.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.297
Teacher spread0.277 · 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

Citations13
Published2009
Admission routes1
Has abstractyes

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