Stripping the Wallpaper of Practice: Empowering Social Workers to Tackle Poverty
Bibliographic record
Abstract
The relationship between deprivation and health and educational inequalities has been well evidenced in the literature. Recent UK research has now established a similar social gradient in child welfare interventions (Bywaters et al. 2018) with children living in the most deprived areas in the UK facing a much higher chance of being placed on the child protection register or in out-of-home care. There is an emerging narrative that poverty has become the wallpaper of practice, “too big to tackle and too familiar to notice” (Morris et al. 2018) and invisible amid lack of public support and political will to increase welfare spending. This paper will examine poverty-related inequalities and how these affect families. It will discuss the importance of recognising that poverty is a social justice issue and a core task for social work and outline the range of supports that may be available for families to help lift them out of poverty. Finally, it will describe the development of a new practice framework for social work in Northern Ireland that challenges social workers to embed anti-poverty approaches in their practice. The framework emphasises that poverty is a social justice issue, seeks to provide practical support and guidance to re-focus attention, debate, and action on poverty in times of global economic uncertainty and give social workers the tools to make it central to their practice once again. It reinforces the need for social workers to understand and acknowledge the impact of poverty, and to advocate for and support those most in need. It aims to challenge and empower professionals to tackle poverty and inequality as an aspect of ethical and effective practice.
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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 | 0.058 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.036 | 0.063 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.005 | 0.062 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".