The Protracted Bargain: Negotiating the Canada–China Foreign Investment Promotion and Protection Agreement
Bibliographic record
Abstract
Summary In 1994, Canada and China began negotiating a bilateral foreign investment promotion and protection agreement (FIPA). After sixteen years and multiple rounds of negotiations, the two states have not been able to solidify a workable treaty. By examining each country’s substantive and procedural preferences in their respective bilateral investment treaty models and in past treaties, this article outlines some of the likely “on-the-table” obstacles in the negotiating process. The analysis indicates that there are areas of considerable convergence between each country’s preferences, although significant areas of divergence exist on some key issues. Further confounding the disagreement that exists between the two countries are “off-the-table” factors such as general bilateral relations. One further aspect that is considered is the idea of coordinating compliance between international trade and human rights norms in the context of the Canada–China FIPA. While bilateral investment treaties are economic agreements, pronounced non-economic elements shape the practical and legal effect that these treaties have on various affected actors. Despite the important implications the Canada–China FIPA has for human rights and environmental policy concerns, it can be inferred that these factors will have little bearing on the actual negotiated outcome of the agreement.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".