From Backdoor-Opening To Concluding “Real” Free Trade Agreements? :\nJapan’s Free Trade Policy Towards The US, The EU And Latin American Countries
Bibliographic record
Abstract
The first country Japan signed an Economic Partnership Agreement (EPA) was Singapore in January\n2002. Since then, Japan has agreed upon EPAs with most of the Asian-Pacific countries, including\nAustralia (July 2014), but without Korea and China. This could be described as the first stage of\nJapan’s FTA/EPA negotiations, set upon a strategy to conclude with countries “as many as possible”\nand “in the order of conclude-able.” Japan’s EPAs with Mexico (May 2005), Chile (September 2007),\nand Peru (March 2012) aimed to catch up with the US and the European Union’s (EU) free trade\npolicy with Latin American countries, so that Japanese multi-nationals would not reduce market\naccess. Japan’s strategy in this stage had been passive. In the current second stage, which the country\nhas gradually entered into after the global financial crisis, Japan pursues active roles and negotiates\nwith “bigger” partners such as the US (in the TPP, Trans-Pacific Strategic Economic Partnership\nAgreement, or Trans-Pacific Partnership), Canada, the EU, Korea and China. Japan’s EPAs with\nMexico, Chile and Peru played the role of opening a backdoor to Japan’s highly protected agriculture\nmarket, therefore urging a new dimension to the country’s foreign trade policy. While allowing\nJapanese farmers to export to Latin America, Japan in turn opened its domestic market of wine, beef,\nchicken, pork and others, which was a compromise the country had rarely made with the US and the\nEC (European Community)/EU during the history of trade conflicts since the 1970s. Besides\nagriculture, Japan is further required to abandon non-tariff barriers and to open public procurement.\nWhether Japan could take a leap from its familiar and “comfortable” regulated free trade policy into\n“real” free trade and therefore play a role in setting rules and accelerating global free trade is\nseriously put into question.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".