Mothering and child protection practice: rethinking risk assessment
Bibliographic record
Abstract
In recent years, across North America and the UK, child protective service agencies have increasingly begun to rely on bureaucratic, technocratic, and regulatory mechanisms for detecting and managing abuse and neglect. Coupled with a shift from concern for the general social welfare of children to a heightened preoccupation with risk or dangerousness to children, risk assessment systems are becoming integral to child protection practice. Though risk assessment systems aim to enhance the effectiveness of child protection investigations and service provision, as well as filter out high risk cases from the rest, such systems may foster and reproduce often concealed relations of gender, race, and class. This paper presents a review of the risk assessment trend in child welfare, its general objectives, and some criticisms raised to date. Through a feminist analysis of the social construction of mothering, we re‐examine risk assessment. We argue that the risk assessment trend has the potential to entrench oppressive relations of gender, race, and class in child welfare practice with mothers. We suggest that an approach to risk assessment which incorporates a ‘mothering narrative’ might offer a more thorough evaluation of the conditions that shape the context within which risk to children emerges.
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 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 | 0.047 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".