Human-Centred Design in UK Asylum Social Protection
Bibliographic record
Abstract
This paper considers United Kingdom welfare provision for asylum seekers in the context of social protection scholarship, policy discourse more commonly associated with international development. Social protection definitions are contested, ranging from those focused on state provision to wider interpretations reflecting debates on holistic wellbeing, human rights and self-actualisation. Most recently, the 2030 Agenda for Sustainable Development has called for social protection policies for all citizens to reduce inequality among and within countries. Though there is exigency to reduce the extreme inequality existing between countries, literature is lacking on how social protection can be used to critique inequality within more economically affluent nations. Commentaries on social protection also tend to focus on economic poverty, with less attention given to vulnerabilities such as marginalisation. Literature suggests that UK asylum welfare provision is based on deterrence, control and marginalisation. In response, and to encourage equity in how all countries’ public policy is assessed, this paper utilises an international social protection framework to critique UK asylum welfare provision. It concludes by advocating for transdisciplinary, human-centred and comprehensive social protection policy design, encouraging participation by a wider range of stakeholders and a holistic understanding of wellbeing to meet asylum seekers’ needs effectively and efficiently.
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 | 0.032 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.050 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".