Ethical dilemmas? UK immigration, Legal Aid funding reform and caseworkers (Respond to this article at http://www.therai.org.uk/at/debate)
Bibliographic record
Abstract
The article considers the kinds of responsibilities anthropologists might have when working on immigration and asylum matters, particularly in the light of recent ‘reforms’ to the funding of legal aid in the UK. The article focuses on a single case study in its context, exploring an interaction between an immigrant applicant and a lawyer/case worker in a not‐for‐profit Law Centre. The paper shows how case workers find themselves caught in the middle, squeezed between increasing financial pressures and their ethical obligation to their clients. In their everyday work they are faced with the contradictory imperatives of giving sympathy and advice to genuinely deserving cases on the one hand while being required to check up on opportunist and possibly deceitful clients on the other. In the context of ‘reform’, they are increasingly encouraged to prejudge the probable outcomes of cases by reference to ‘value for money’. These effects, we argue, have severe consequences given the adversarial nature of the UK's system of law. As anthropologists, our aim was to investigate the day‐to‐day realities of case worker/client interactions, to enable both ourselves and legal practitioners, in conversation, to reflect on our own and each others' interpretations of the situation, and to place these in the public domain.
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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 | 0.041 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.052 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.023 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".