Vergelijking van 1F-uitsluitingen van Syrische asielzoekers in Nederland en België
Bibliographic record
Abstract
Vermoedens van betrokkenheid bij oorlogsmisdrijven en andere ernstige misdrijven kunnen leiden tot uitsluiting van asielbescherming op basis van artikel 1F Vluchtelingenverdrag. Hoe wordt dit artikel toegepast bij Syrische asielzoekers, sinds het begin van de Syrische burgeroorlog? Maarten Bolhuis, Tara Ditzel en Joris van Wijk bespreken de overeenkomsten en verschillen tussen Syrische 1F-zaken in Nederland en België. De verschillende standaarden van Nederlandse en Belgische rechters komen aan de orde, evenals verschillen in interpretatie van pendanten van schulduitsluitingscriteria, zoals overmacht en dwang. Daarbij blijkt dat de Nederlandse rechter artikel 1F ruimer toepast dan de Belgische, met name in verband met indirecte betrokkenheid bij 1F-misdrijven en de interpretatie van die pendanten.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".