Bibliographic record
Abstract
The Canada-European Union Comprehensive Economic and Trade Agreement (CETA) has been called a game-changer. In the investment chapter, CETA has introduced a number of key innovations, including the investment court system with an appellate tribunal guidelines on third party funding transparency and information sharing modern versions of standards of protection detailed provisions on reservations and exceptions Considering that the new dispute resolution provisions in this chapter have also passed the scrutiny of the Court of Justice of the European Union, it is expected that CETA’s investment chapter will serve as a blueprint for future EU investment agreements. This article-by-article commentary will be a key resource for practitioners and academics in the field of EU investment protection law. With contributions by Afolabi Adekemi, Andrés E. Alvarado Garzón, Freya Baetens, Crina Baltag, Jens Benninghofen, Christina Binder, Gabriel Bottini, Colin Brown, Marc Bungenberg, Markus Burgstaller, N. Jansen Calamita, Armand de Mestral, Arnaud de Nanteuil, Lori Di Pierdomenico, Patrick Dumberry, Katia Fach Gómez, Richard Happ, Angshuman Hazarika, Stephan Hobe, Frank Hoffmeister, Anna Holzer, Mattijs Kempynck, Panos Koutrakos, Ursula Kriebaum, Céline Lévesque, Irmgard Marboe, Lars Markert, Patricia Nacimiento, Erman Özgür, August Reinisch, Stefanie Schacherer, Julian Scheu, Christoph Schreuer, Lukas Stifter, Johannes Tropper, Güneş Ünüvar, Lukas Vanhonnaeker and Herman Verbist.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".