Beyond Territoriality: The Case of Transnational Human Rights Litigation
Bibliographic record
Abstract
Cases for civil damages that have been brought before Western courts by victims of torture and persecution against states officials or corporations, challenge the principles of state sovereignty and jurisdictional competence. While national courts can in cases of serious crimes hear cases that grow out of acts committed in another country, the same is not true for cases for civil compensation. A persisting and rising number of private law cases that attempts to empower disenfranchised victims of crime and abuse, points to the necessity of reconsidering the prevailing procedural and substantial obstacles that govern the so-far unsuccessful civil law suits. The law of transnational civil litigation [TCL] emerged with the US American decision in Filartiga in 1980 and perhaps culminated in the US Supreme Court's Decision in Sosa v. Alvarez-Machain in 2004. TCL has become a laboratory for our inquiry into the relationship between laws that were developed within and for the nation-state on the one hand and an increasingly globalized political and legal human rights discourse, on the other. As such, TCL is a case in point for the dramatically changing nature of norm-creation, law, and law enforcement in an era of globalization.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.018 | 0.013 |
| 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".