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Record W4206235530 · doi:10.1093/cjcl/cxac001

Applying the CISG to Hong Kong: Legal Analysis and Policy Recommendations

2021· article· en· W4206235530 on OpenAlexaboutno aff
Qiao Liu, Jiangyu Wang

Bibliographic record

VenueThe Chinese Journal of Comparative Law · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicConflict of Laws and Jurisdiction
Canadian institutionsnot available
Fundersnot available
KeywordsChinaNegotiationBusinessInternational tradeProduct (mathematics)ConventionLawPolitical science

Abstract

fetched live from OpenAlex

The Untied Nations (UN) Convention on Contracts for the International Sale of Goods (CISG), which was adopted by a UN conference in 1980 and came into effect in 1988, is one of the most successful treaties in international commercial law in the sense of establishing a uniform legal framework for international trade in goods. A product of comparative law, it provides a set of default rules to govern cross-border sale of goods in regard to the formation of contracts, obligations of buyers and sellers in performing contracts, and legal remedies for breach of contract, among other issues.1 The CISG has been ratified by 94 countries, including many of the world’s most important economies such as the USA, China, France, Germany, Japan, Brazil, Australia, Canada, to name but a few.2 Specifically, China, an active participant in the UN-led negotiations for the CISG, ratified the CISG in 1986. On 27 May 2019, in response to a proposal made by the Department of Justice (DoJ) of the Hong Kong Special Administrative Region (HKSAR) government, the Panel on Administration of Justice and Legal Services of the Legislative Council (Legco) approved that a public consultation be held to solicit views on the proposed application of the CISG to Hong Kong.3 A consultation paper (CP), whose Annex 4.1 contains a draft of Sale of Goods (United Nations Convention) Bill, was subsequently publicized. The report entitled Policy Recommendations for the Proposed Application of the CISG to Hong Kong’, which is reprinted below, was prepared in response to the CP and submitted in September 2020. The DoJ made a proposal to the Legislative Council for discussion on 22 March 2021 after considering all the submissions received.4 In the revised proposal, the following suggestions made by the authors in the report were adopted :

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)0.020
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score0.568

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0040.004
Scholarly communication0.0130.009
Open science0.0040.004
Research integrity0.0100.007
Insufficient payload (model declined to judge)0.0230.003

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 head0.035
GPT teacher head0.397
Teacher spread0.361 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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