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Record W2490244506 · doi:10.1108/jitlp-10-2015-0027

US phytosanitary restrictions: the forgotten non-tariff barrier

2016· article· en· W2490244506 on OpenAlexaboutno aff
Marie-Agnès Jouanjean, Jean–Christophe Maur, Ben Shepherd

Bibliographic record

VenueJournal of International Trade Law and Policy · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsPhytosanitary certificationPoliticsMarket accessTariffOriginalityScope (computer science)International tradeBusinessValue (mathematics)Work (physics)EconomicsInternational economicsPolitical scienceAgricultureEconomic growthLaw

Abstract

fetched live from OpenAlex

Purpose This paper aims to provide new evidence that the US phytosanitary regime is associated with a restrictive market access environment for fruit and vegetable products. One chief reason seems to be that the US regime uses a positive list approach, under which only authorized countries can export. Design/methodology/approach The methodology of the paper is primarily qualitative. This paper reviews the US sanitary and phytosanitary measures (SPS) system and its scope for use to protect markets, in addition to protecting life and health. The approach is institutional and political economic. Findings For most products, only a portion of global production is authorized for export to the USA. Even among authorized countries, only a small proportion is actually exported. As a result, the number of countries exporting fresh fruit and vegetables to the USA is far lower than those exporting to countries like the EU and Canada, but it is on a par with markets known to be restrictive in this area, such as Australia and Japan. Using a data set of fruit and vegetable market access and political contributions, this paper also provides evidence showing that domestic political economy considerations may influence the decision to grant market access to foreign producers. Originality/value The US SPS system has not previously been analyzed in this way, and the distinction between negative and positive list approaches is highlighted in terms of its implications for third-party exporters. Similarly, the analysis of political contributions is novel and suggestive of an important dynamic at work in the determination of the US policy.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.237
Teacher spread0.207 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations14
Published2016
Admission routes1
Has abstractyes

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