Bibliographic record
Abstract
vant interpretive principles like the principle of non-retrogression. What is the relationship between the Vienna Convention and human rights, humanitarian, and refugee treaties? Is nonretrogression a free-standing principle of treaty interpretation? As the case of Suresh v. Canada (Minister of Citizenship and Immigration) illustrates, such questions are more than academic. The Federal Court of Appeal in this case used the 1951 Refugee Convention to undercut the absolute right to be free of torture as recognized in the Torture Convention. The above points are not meant to detract from any particular paper or from the collection as a whole. Rather, they underscore the complexity and timeliness of the convergence problem. Those concerned with the human rights of refugees and the internally displaced from dispossession to refuge to settlement or repatriation will find Human Rights and Forced Displacement a valuable book. Those interested in the more general question of the cross-fertilization of international regimes will also find it worthwhile. One hopes that this collection of essays will inspire scholars and advocates alike to dedicate more time and energy to the issues surrounding convergence, compatibility, and cross-fertilization of legal traditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".