How can we talk about child protection without talking about child protection?
Bibliographic record
Abstract
Language used by professionals when describing or speaking with parents of children within child protection services can be stigmatizing and harmful. Professionals across health, welfare and childhood education sectors frequently encounter parents who are experiencing multiple social, economic, and health adversities that impact their children's health, development, wellbeing , or physical safety. Families experiencing multiple adversities are often caught within intergenerational cycles of disadvantage and marginalisation which are difficult to escape. A public health response to child protection responds to those experiencing adversities and provides prevention, early support, and responses before situations escalate to where children's safety is under threat. Nurses, social workers, physicians, midwives , and lawyers are some examples of professionals who form the broader network of health, welfare, and early childhood professionals who are well-placed for child protection public health responses to prevention and early intervention that empowers families to disrupt intergenerational disadvantage. Language used throughout society and across health, welfare and education sectors to respond to families experiencing adversities including violence and maltreatment may inadvertently further marginalise these families and reduce their willingness to seek and accept support. This language has particularly significant consequences when used across government policy, health and welfare systems and by service providers and practitioners to enact a child protection public health response. This paper firstly summarises the historical and social context shaping the language used when supporting families impacted by violence and child maltreatment, and concludes with strategies to address unhelpful language that can perpetuate marginalisation and stigma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".