Exploring the Role of Social Protection in UK Asylum-Seeker Wellbeing Using Human Scale Development Theory
Bibliographic record
Abstract
This article utilises Max-Neef’s Human Scale Development (HSD) framework (1991) to answer two research questions: what impact does government and community-based social protection (SP) have on UK asylum-seeker wellbeing; how are interactions with all forms of SP, both as giver and receiver, supporting or harming the satisfaction of asylum-seekers’ fundamental human needs at this time? The research study utilised a mixed-methods, collaborative, case study design situated within a refugee and asylum-seeker (RAS) support charity in Southwest England. Methods included peer-led Qualitative Impact Protocol interviews, Photovoice, surveys, and staff interviews. Data were subjected to an inductive, bottom-up process on Causal Map software (version 2, Causal Map Ltd., 39 Apsley Rd., Bath BA1 3LP, UK) and the analysis used the HSD framework. We found eight over-arching themes. The four main needs-violators/destroyers of asylum-seeker wellbeing were dehumanisation, unfreedoms, enforced ignorance, and (re)traumatisation, and the four main needs-satisfiers were common humanity, autonomy and resistance, exerting agency through knowledge exchange, and healing. Five policy and practice-focused bridging satisfiers are recommended to help move individual and collective experience from a negative to a positive state in the research population. Policy and practice should be transparent and evidence-based, efficient and equitable, supportive of participation and productivity, trauma-informed, and multi-agency.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".