The Next Generation of Investment Treaties and their Impact on Investor-State Dispute Settlement
Bibliographic record
Abstract
Hogan Lovells and Notre Dame Law School are proud to host a lecture on “The Next Generation of Investment Treaties and their Impact on Investor-State Dispute Settlement.” The lecture will be held from 5 -7p.m. on Thursday, February 12, 2015 at the City Club of Washington, D.C., 555 13th Street NW. A cocktail reception will follow. Our keynote speaker for the evening will be Meg Kinnear, Secretary-General of the International Centre for Settlement of Investment Disputes (ICSID) at the World Bank. Prior to joining ICSID in 2009, Ms. Kinnear was General Counsel, Senior General Counsel, and Director General of the Trade Law Bureau of Canada, where she advised the Government of Canada on international investment and trade law, participated in investment and trade treaty negotiations, and represented Canada in NAFTA Chapter 11 arbitrations. She is a frequent speaker and author in this field. The event will feature: • Meg Kinnear, ICSID Secretary- General, World Bank, Keynote Speaker • Roger Alford, University of Notre Dame, Commentator • Jonathan Stoel, Hogan Lovells, Commentator • Horacio Grigera Naón, American University, Commentator • Susan Franck, Washington and Lee University, Commentator • Michael Tracton, U.S. Department of State, Commentator
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".