Reflexivity in moral decision-making: assessing and managing child abuse and domestic violence
Bibliographic record
Abstract
Many countries are reporting increases in domestic violence and child abuse rates and intensity during the Covid-19 pandemic. This pervasive, complex, and sensitive issue requires careful responses from child welfare organisations. Signs of abuse – often hidden and open to interpretation - can be difficult to detect and assess, and processes rely on the personal judgement of social workers and professionals. Acknowledgement of the complexities of abuse has led to attempts at reorganising the field and reducing uncertainties of practice through the provision of standardised assessments, guidelines and instruments, a trend also observed in the Netherlands. Concurrently, there is a recognition of the importance of professionals’ personal judgement and the need for reflexive practice and use of different knowledges, such as intuition. There is an opportunity for guidelines to support reflection and intuition as part of ‘good care’ practice, but we have found that on the ground, professionals fear malpractice accusations. By conducting in-depth interviews with professionals and observations of their multi-disciplinary discussions, this paper explores how good reflexivity can be achieved in highly charged moral settings at the intersection of the multiple marginalizations of domestic violence and child abuse. We particularly focus on how professionals navigate complexities, and what the role is of regulatory infrastructures, including guidelines and protocols in framing their interaction with the child welfare system.
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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 | 0.180 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".