“Imposter-Children” in the UK Refugee Status Determination Process
Bibliographic record
Abstract
This article describes and analyzes an emerging problematic in the asylum and immigration debate, which I cynically dub the “imposter-child” phenomenon. My preliminary exploration maps how the imposter-child relates to and potentially influences the politics and practices of refuge status determination in the United Kingdom. I argue that the “imposter-child” is being discursively constructed in order to justify popular and official suspicion of spontaneously arriving child asylum-seekers in favour of resettling refugees from camps abroad. I also draw connections between the discursive creation of “imposter-children” and the diminishment of welfare safeguarding for young people. Further complicating this situation is a variety of sociocultural factors in both Afghanistan and the United Kingdom, including the adversarial UK refugee status determination process, uncertainty around how the United Kingdom can“prove” an age, and a form of “triple discrimination” experienced by Afghan male youth. Through unearthing why the “imposter-child” is problematic, I also query why it is normatively accepted that non-citizens no longer deserve protection from the harshest enforcement once they “age out” of minor status.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.034 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".