Rewriting Refugee Law: Centring Refugee Knowledges and Lived Experience
Bibliographic record
Abstract
This Special Issue is part of an initiative co-led by scholars and lawyers with lived experience of (forced) displacement. We use the term “displacement” expansively to include all forms of displacement that compel people to leave their places of habitual residence whether as a result of human rights violations, colonisation, slavery, human trafficking, violent conflict, natural disaster, environmental conditions, climate change, or corporate development. However, in the context of this Special Issue, rewritten judgments focus on refugees, a term we also use broadly – and interchangeably – with displaced persons as a way of ensuring that neither our1 lived experience nor our jurisprudential imaginations are constrained or invalidated by imposed legal categories. Our purpose is to rethink, reframe, and rewrite judicial decisions critically, informed by the perspective of displaced persons’ lived experience. Legal frameworks that govern significant aspects of (forced) displacement and the institutional and professional fora created to administer them are colonial, racialised,2 and patriarchal. These legal frameworks, and the way they are understood and practised, govern who is or is not defined as needing or deserving of international protection.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.057 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| 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".