Las Industrias Extractivas Canadienses Y Su Impacto Sobre Los Derechos Humanos: Esperando a Trudeau (Canadian Extractive Industries and Their Impact on Human Rights: Waiting for Trudeau)
Bibliographic record
Abstract
Spanish Abstract: En esta Nota se pone de relieve el impacto de las industrias extractivas canadienses sobre los Derechos Humanos, en lo que se refiere a sus actividades en el extranjero. Se recuerdan, por ejemplo, la iniciativa C-300, y diversos casos celebres, como la Masacre de Kilva, en la RDC, o Anvil Mining, en el Sudan, y recientes informes internacionales que analizan detalladamente el problema. Contando con ello, la Nota urge al Premier Trudeau a que adopte iniciativas legislativas para el control de las citadas empresas, preferiblemente bajo la forma de normas imperativas y extraterritoriales. Y sintonizando asi con la creciente conciencia mundial sobre la necesidad del referido control, del que tambien se vienen haciendo eco los tribunales canadienses, por ejemplo, en los casos Choc y Nevsun.English Abstract: All along this Note we highlight the impact of the extractive Canadian industries on Human Rights, as far as their activities abroad are concerned.It is recalled, for instance, the initiative C-300 and different notorious cases, such as the Massacre of Kilva, in the Democratic Republic of Congo (DRC), or Anvil Mining in Sudan, and recent international reports analyzing the problem in detail. Counting on that, the Note urges Premier Trudeau to take legislative initiatives in order to put the above mentioned companies under control, preferably with mandatory and extraterritorial rules. In this way we tune with the growing global awareness over the need concerning the referred control, echoed by the Canadian Courts as well, for example, in the cases Choc and Nevsun.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".