“The Best Risky Point”: Agency and Decision-Making in Young Unaccompanied Asylum Seekers' Stories of Leaving Home and Travelling to Australia
Bibliographic record
Abstract
Seeking asylum is a perilous endeavour with unpredictable border crossings, protection prospects, and settlement outcomes. Young unaccompanied asylum seekers face even greater risks. Yet exclusively characterizing them as vulnerable or passive ignores their agency in making choices in a range of unique, dynamic, and challenging circumstances. In this article, we use deep ethnographic methodology to amplify young asylum seekers’ voices, examining their capacity to enact agency along the asylum journey. We employ Bourdieu’s non-doxic contexts and Jackson’s “border situations” to describe the unstable environments young people navigate at home and during their journey to Australia. Our findings reveal a nuanced picture of young people both as objects of other people’s decisions (with reduced agency) and as highly engaged in dynamic decision-making during their journey to Australia (with more salient agency). These findings indicate the importance of research methods that steer away from fixed assumptions around vulnerability and victimhood to recognize the agentic capacity of young people to make life-defining decisions even as they find themselves in transnational border situations that seek to control and constrain them.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".