Expert Consultation on Corporate Law and Human Rights (November 2009)
Bibliographic record
Abstract
On November 5 and 6, 2009, Osgoode Hall Law School convened a major workshop, the Expert Consultation on Corporate Law and Human Rights: Opportunities and Challenges of Using Corporate Law to Encourage Corporations to Respect Human Rights.\nThe co-convenors of the Expert Consultation were Osgoode’s Professor Aaron Dhir and Professor Sara Seck of University of Western Ontario Faculty of Law. The Nathanson Centre was a major sponsor along with the Office of the United Nations High Commissioner for Human Rights, Export Development Canada and PricewaterhouseCoopers. York University’s Hennick Centre for Business and Law also provided assistance in the planning and implementation of the consultation.\nThe expert consultation, held at Osgoode Professional Development Centre, brought together corporate lawyers, civil society, academics, government regulators and industry representatives in support of the Corporate Law Tools Project of the Special Representative of the UN Secretary-General on Business and Human Rights, Professor John Ruggie.\nThe multi-stakeholder group of experts discussed how key corporate and securities law concepts such as incorporation and listing; directors’ duties; reporting; shareholder engagement; and other corporate governance issues as expressed in national laws and guidelines support companies to respect human rights. Professor Ruggie also participated in a lunchtime seminar with Osgoode Hall students, faculty and staff.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.036 | 0.015 |
| Insufficient payload (model declined to judge) | 0.069 | 0.017 |
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".