A reinterrogation of South African child welfare discourse: A case for decolonisation?
Bibliographic record
Abstract
Abstract Relying on discourse analysis and critical social work, this article explores the relevance of a decolonisation discourse to South African child welfare. A child welfare discourse of coloniality emerges from Australia, New Zealand and Canada. This emphasises the role that colonisation has played in eradicating indigenous persons or alternately assimilating subjugated populations to Western norms and sensibilities and maintains that coloniality persists in contemporary child welfare. South African child welfare has not been explicitly problematised as furthering coloniality. There have been transformation efforts post-apartheid relating to the legislative/policy environment and increasing racial representation and community-based access. However, the colonial and apartheid roots of South African child welfare persist in impacting child welfare, particularly by overriding local ways of being. A decolonisation discourse is needed to identify the various ways in which the child welfare system replicates colonising processes and how these can be interrupted. To do so, the individualised, intrusive, punitive, statutory Child Protection discourse must be replaced, structural issues prioritised, intergenerational and contemporary trauma centred, liberatory indigenous child-rearing practices privileged and local knowledges curated and used to inform the child welfare process.
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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 | 0.029 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.095 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".