Unaccompanied Minors in International, European & National Law by Ralf Roßkopf
Bibliographic record
Abstract
Ralf Roskopf’s edited book, Unaccompanied Minors in International, European and National Law (hereafter Unaccompanied Minors) dissects an in depth comparative analysis of current legal standards of protection in seven countries in Europe and North America. Europe, the European Union, Canada and the United States are the central focus of the book with particular regard to those already arriving in these countries. Little or no reference is made to the largest refugee host countries (i.e., Syrian Arab Republic, Afghanistan and Somalia), which house more than half of all refugees worldwide. Neither is any mention made of four of the top ten countries in Africa (i.e., Nigeria, Democratic Republic of the Congo, Central Africa Republic and South Sudan) that likely experienced new violence-induced internal displacements in 2015. With three out of every five international child migrants living in Asia or Africa, the growing crisis for unaccompanied migrant children also needs to be assessed at its roots. Considering the vulnerabilities, trauma and risks faced by unaccompanied minors, as noted by UNICEF above, may not only assist Europe, Canada and the United States in creating national legal standards that are reflective of migrant children experiences; but, may also go a long way to develop comprehensive intervention programs to curb the flow of unaccompanied migrant children.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".