"I See Nothing but a Fence of Tears": The Impact of Australia's Immigration Detention and Border Protection Policies on the Asylum Seeker Child's Geographies of Hope and Hopelessness
Bibliographic record
Abstract
As a signatory to both the United Nation Refugee Convention and the United Nations Convention on the Rights of the Child, Australia's border protection policy to detain offshore asylum seekers who reach Australian borders by boat, including accompanied and unaccompanied minors, is under intense international scrutiny. In the context of Australia's "Operation Sovereign Borders," however, the asylum seeker child's perspectives and their geographies of hope and hopelessness have not yet been fully explored. Drawing on recent literature within children's geographies, which emphasizes the "emotional" matters within policy development and professional practice, and how they affect children, this paper seeks to contribute to emerging debates exploring borders, asylum seeker children, and children's emotional geographies. Utilizing drawings, letters, and poems produced by children for an Australian Human Rights Commission's National Inquiry into Children in Immigration Detention on Nauru, a child-centred approach was applied to privilege children's own perspective of their indefinite internment. The primary focus of the paper is to emphasize the ways in which "the asylum seeker child" constructs their own emotional geographies within the inherently complex and restrictive context of Australia's border protection policy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".