Interrogating the efficiency paradigm : a study of language analysis as evidence in refugee status determination
Bibliographic record
Abstract
In 1993, Sweden commenced the unprecedented practice of using Language Analysis (LA) as evidence in refugee status determination. Since that time, Western governments trying to cope with the perceived refugee crisis have similarly adopted the tool to corroborate and undermine the nationality claims of asylum seekers crossing borders without identity documents. During this same period, language professionals, lawyers, various news media, and others across the globe have proceeded to fuel international controversy on the subject, largely challenging the linguistic integrity of the tool, while investing less energy addressing the political context of use, as well as the implications for violations of refugee rights. In 2007, Canada reflected prioritized concerns for efficiency when it made public a pilot project to address the value of this language tool in aiding status decision-making. This paper interrogates the Canadian efficiency paradigm through the Australian lens of LA in practice. In exposing the ethical and legal sites of likely disengagement should Canada proceed with implementation, this paper cautions against LA becoming the most recent assault on a Canadian protection regime already under siege.
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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 | 0.139 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.024 | 0.160 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".