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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 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.035
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 teacher head, not a consensus.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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