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Record W1960587127 · doi:10.1111/cfs.12231

Looking under the bonnet: probation officers' practice with child protection cases

2015· article· en· W1960587127 on OpenAlexaff
Maria Ansbro

Bibliographic record

VenueChild & Family Social Work · 2015
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsQueen's University
Fundersnot available
KeywordsChild protectionAgency (philosophy)HarmOfficerSocial workCriminologyRestructuringWork (physics)PsychologyPublic relationsPolitical scienceService (business)Social psychologyLawSociologyBusinessEngineeringSocial science

Abstract

fetched live from OpenAlex

Abstract This research examined 31 Probation Service cases inEngland andWales that required some child protection work. It examined three areas: key characteristics of the case, inter‐agency communication and evidence for a ‘think family’ approach. Key findings were that domestic violence and substance misuse were widespread. The majority of the cases were assessed as low or medium risk of harm, and so after the ‘Transforming Rehabilitation’ restructuring will be supervised by aCommunityRehabilitationCompany, rather than theNationalProbationService. The majority of cases featured effective inter‐agency communication, and were characterized by the ‘think family’ principle. Where this was not achieved, two types of cases stood out. The first was where the probation officer was distracted from offenders' children because of public protection issues. The second was where probation officers made efforts to be included in multi‐agency work, but were shut out.

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)0.005
metaresearch head score (Gemma)0.035
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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.035
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0110.005
Scholarly communication0.0030.002
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.045
GPT teacher head0.294
Teacher spread0.249 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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