Jen Moore v. Minister of the Interior of Peru et al (Amicus Curiae Brief)
Bibliographic record
Abstract
This amicus curiae brief offers international law analysis to the Primer Juzgado Penal con Reos Libres in Cuzco Peru in relation to the Court’s consideration of Canadian citizen Jennifer Moore’s habeas corpus claim against the Ministry of the Interior, the Peruvian National Police (PNP), the PNP’s Department of State Security of Cusco, and the Cusco Regional Headquarters of the Superintendent of National Migration. The amicus curiae brief elaborates on the instruments of international law and policy that are relevant to the human rights aspects of Moore’s claim. It begins with a review of the official documents pertaining to the actions of the Defendants to apprehend Moore and subsequently prohibit her from re-entering Peru. On the basis of the information reviewed, we conclude that the actions and decisions of the Defendants violate the rights to free expression (Article 13), free association (Article 16) and free mobility (Article 22) as protected by the American Convention on Human Rights. Additionally, we note that the Defendants’ rationale for the ban on Moore in the circumstances of this case has the potential to adversely affect the human rights of anyone who visits Peru on a tourist visa and shares information or expresses views that are critical of the PNP or foreign mining companies. This highlights the international importance of this case.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".