Keikutsertaan Kanada dalam Perjanjian Trans-Pacific Partnership: Sebuah Analisis Liberal Intergovernmentalism
Bibliographic record
Abstract
In October 2012, Canada became one of the members of the Trans-Pacific Partnership (TPP) agreement negotiation, a blueprint of the largest free trade agreement in history that covers 12 Asia-Pacific countries and 40 percent of the world economy. This agreement was then signed by Canada and 11 other member countries in February 2016. Canada once rejected the invitation to join this agreement when it was still called TPSEP in 2005, but instead became observer and expressed its interest to join several years later. Canada’s wish to join was met by rejection from some of the TPP members, as well as from their own domestic social groups. After the signing, the rejection increased and Canadians considered that their government had failed to carry on their interests during the negotiation. Therefore, Canada’s approval on TPP despite disagreements from many groups was something peculiar. In this article, the author would look into this phenomenon through liberal intergovernmentalism, an international relations theory developed by Andrew Moravcsik focusing in regionalism. The first part of the article will discuss the methods of liberal integovernmentalism theory, whereas the second and third part will discuss the result of the analysis and the conclusion from this article. Keywords: TPP, Liberal Intergovernmentalism, Canada, preference
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".