The Security Dimension of a China Free Trade Agreement: Balancing Benefits and Risk
Bibliographic record
Abstract
In 2017, Canada engaged in several rounds of exploratory discussions for a potential free trade agreement (FTA) with the People’s Republic of China. It seemed probable that this exploratory phase would be followed by the opening of formal rounds of negotiations, to be announced during Prime Minister Justin Trudeau’s visit to China in December 2017. An FTA appeared to be a priority for Trudeau since his government came into office in 2015 (Global Affairs Canada, 2017a; Lu, 2017; PMO, 2017); however, such negotiations were put on hold indefinitely, ostensibly due to irreconcilable differences on gender and labour issues. Despite this setback, it is likely that the Canadian government will continue to explore this option in the coming years, particularly with the North American Free Rade agreement (NAFTA) in jeopardy. While there are many potential benefits of a Canada-China free trade agreement (CCFTA), there are also significant national security implications that will deserve particular attention. The security dimension will be the focus of this paper.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".