Preface: Yearbook on International Investment Law and Policy, 2010-2011
Bibliographic record
Abstract
Despite the Western financial crisis and recession in 2008, FDI shows signs of expansion. This growth underlines the importance of a stable and predictable international investment law and policy regime to quell investor worries of the risk of political upheavals, debt burdens, natural disasters, and protectionist policies. Furthermore, the renegotiation of investment treaties offers established actors an opportunity to refine the international investment law and policy regime and change its direction. As the regime is defined and redefined, key actors will have to confront debate surrounding security clauses, acquisition of land and governance conditions. This overview is part of a volume that contains chapters by Jose E. Alvarez and Tegan Brink; Michael D. Nolan and Frederic G. Sourgens; Alberto Alvarez-Jimenez; Katia Fach Gomez; Stephan W. Schill and Yun-I Kim; Daniel M. Firger and Michael B. Gerrard; Olivier De Schutter and Peter Rosenblum; Mark Feldman; Philippe Pinsolle; O. Thomas Johnson Jr. and Jonathan Gimblett; David Schneiderman; Louis T. Wells and Chieko Tsuchiya; Parvan P. Parvanov and Mark Kantor; Clint Peinhardt and Todd Allee; Gus Van Harten; Susan D. Franck, Calvin P. Garbin, and Jenna M. Perkins.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.133 | 0.123 |
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".