Evaluation of serious case reviews and anti-racist practice
Bibliographic record
Abstract
The aim of this chapter is to consider how findings from recorded serious case reviews (SCRs) that took place during the 1991–2010 period informed or otherwise the development of good practice with black and minority ethnic (BME) children and their families residing in England and Wales. The aims are to consider cultural, linguistic, religious and other service needs within reviews and to examine key principles that may transform professional practice. The primary research question will be as follows: to what extent are issues of culture, language, religion and ethnicity incorporated in the findings of SCRs? The secondary question relates to the lessons learnt on the needs of BME children and families. Social indicators on income, poverty and housing infer that some BME groups are overwhelmingly disadvantaged in British society. Statistics on looked after children (LACs) suggest that some BME children are not only over-represented, but also likely to remain in care longer than other population groups (Owen and Statham, 2009). Social workers are increasingly seeking to develop practice methods with BME service users and to have in place review systems that address the impact that they and their organisations have on the lives of individuals and groups. However, challenges brought about by austerity measures and other service demands tend to overwhelm the commitment to targeted improvement in services to some groups. Professionals holding child protection responsibilities are particularly prone to pressures triggered by the detailed scrutiny of practice following a serious incident, the excessive focus on negative messages in review findings and the all-too-frequent public scapegoating of the professionals involved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.098 | 0.408 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".