Bibliographic record
Abstract
In an increasingly globalized world, with eroding state borders, and the emergence of a host of non-state actors as powerful players in the international arena, the status of the transnational corporation in international law, especially in terms of criminal liability, remains murky at best. Generating revenue in the billions and accounting for a huge share of Canada's GDP, the extraction industry has spawned a number of Canadian corporations that operate all over the world, often in conflict zones. Despite being embroiled in myriad human rights abuses and having lawsuits leveraged against them, these corporations have enjoyed impunity for what we, post WWII, have recognized as international crimes warranting prosecution. This essay will explore, through the use of two Canadian case studies, the extent to which human rights norms apply to corporations, and their extraterritorial liability for violations under international and domestic law. Furthermore, it will examine the legal avenues in Canada, through which plaintiffs seeking justice can make their case for extraterritorial adjudication of matters involving international crimes committed by Canadian corporations.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".