«On fait du travail social en fait» : Perceptions de leur rôle par les avocat‑e‑s dans le cadre du processus de détermination du statut de réfugié
Bibliographic record
Abstract
Abstract The collaborative involvement of legal and healthcare professionals is often crucial when managing the consequences of the difficult experiences of those seeking asylum and the impact of these on the construction of the asylum application itself. While such collaboration is not always possible, this article focuses on the experiences of lawyers specialized in immigration law, who are often faced with challenges that do not fall strictly within the legal sphere but must be understood in order to support a successful asylum claim. This article examines the different perceptions among these lawyers as to the scope and limits of their role in this context. Some place greater emphasis on the distinction between professions and the limits of each person’s role. Others appear to express a more nuanced perspective, proposing specific strategies to better manage certain aspects related to mental health in particular.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".