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Record W4251564332 · doi:10.1162/asep_a_00725

Summary of the General Discussion on “Defending the Rule-based Trading Regime: The Multilateral Trading System at Risk and Required Responses”

2019· article· en· W4251564332 on OpenAlexaboutno aff

Bibliographic record

VenueAsian Economic Papers · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsCitationIconChinaDownloadPolitical scienceBusinessComputer scienceLibrary scienceWorld Wide WebLaw

Abstract

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Lu Ming started the discussion by asking whether Fukunari Kimura, the author, viewed the current U.S.–China conflict as similar to or different from the U.S. conflict with Japan in the 1980s. Kimura responded that they are different because of two factors: (1) China's exceptional growth compared with Japan; and (2) the fact that Japan had to consider the implications of its economic policies for its national security policies since Japan was tied to the U.S. umbrella, whereas China is free to decide its trade policies from a pure economic perspective.Prema-chandra Athukorala cautioned that Kimura's support of free trade agreements (FTAs) should be preceded by investigations of whether they are worth the effort. More broadly, Athukorala argued that only 30 percent of world trade is in FTAs even though 70 percent of countries have FTAs. In response, Kimura argued that the small share of trade routed through FTAs is not a problem, because the combination of MFN and zero percent tariffs on many goods eliminates the need for FTAs in many cases. As a result, Kimura argued that the primary risk of the current trade conflict is the risk of changes to zero percent tariff trade, which currently constitutes roughly 60 percent of global trade.Athukorala also advocated for the pursuit of WTO-based intellectual property agreements among like-minded countries, similar to the plurilateral information technology agreements that have been concluded. On this point, Kimura agreed that all information technology agreements that can be concluded on an MFN basis should be pursued.Maria Socorro Gochoco-Bautista questioned Japan's ability to take on a leadership role in the conclusion of mega-regional agreements. In particular, given Japan's history of acquiescing to U.S. pressure on trade matters, and as a U.S. ally that may be inclined to take the side of the United States, Gochoco-Bautista questioned whether developing countries could trust Japan to take their side. Similarly, Jin Kyo Suh argued that WTO decisions are predominantly shaped by only a handful of countries, including the United States, EU, China, and sometimes India. In this context, Suh said it would be impossible to do anything without action from these countries, and as a consequence, Suh asked, how could those countries do anything. In response, Kimura noted that Japan, as major, but not super, power, must maintain its support of rule-based trade. Kimura acknowledged that developing countries often view trade negotiations as being shaped by the interests of rich countries. However, he argued that the involvement of developing countries is essential to further progress. At the same time, Kimura argued that the absence of the United States from institutions such as the WTO would also lead to poor outcomes for developing countries. Currently, the U.S. “poison clause” in the USMCA, which precludes Canada or Mexico from negotiating trade agreements with non-market economies without first receiving U.S. agreement, prevents Canada or Mexico from pursuing trade agreements with China.Vu Quoc Huy asked why state-owned enterprises (SOEs) are an issue now, noting that the issue could either be raised by concerns about the activities of SOEs within China, or by the FDI activities of SOEs overseas. Here, Kimura argued that the primary issue of contention is China's provision of subsidies to its SOEs.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0040.005
Scholarly communication0.0060.008
Open science0.0040.002
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0230.008

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.

Opus teacher head0.027
GPT teacher head0.206
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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