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Record W2752149298 · doi:10.5539/jpl.v10n4p118

Loss Compensation Practices in International Sales

2017· article· en· W2752149298 on OpenAlexvenueno aff
Mohsen Hodssein Abadi, Alireza Azadi Kalkoshki

Bibliographic record

VenueJournal of Politics and Law · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsnot available
Fundersnot available
KeywordsDamagesObligationCompensation (psychology)LiabilityConventionBreach of contractBusinessPosition (finance)LawUnificationLaw and economicsActuarial scienceEconomicsFinancePolitical scienceComputer science

Abstract

fetched live from OpenAlex

By concluding any sale, its results works are swiped salesperson and the buyer. The origin of these commitments and traces should be considered the will rule in the conclusion. The contract parties are obliged to do something or refuse to take the current. The commitments of the parties to perform the contract content is interpreted "contractual liability". Contract terms and principles and its loss compensation remedies in international sales conventions of goods and rights of Iran and some countries such as France, Egypt, Lebanon don’t have the greatest difference and hasn’t seen any major hurdle in the way of Iran to join the Convention. Because of the methods of loss calendar and the conditions of its time and site setting in the Convention is workable in the Rights of Iran. But some of the ways to compensate for damages caused by contract defects that are not predicted in the International Convention such as paying the interest are not accepted in Iranian laws. Compensation for damages arising from the breach of contractual obligations needs to be injured in a position that if the obligation was done under the contract, have been fixed in that situation. In the international commercial contracts, principles of the private law unification institute, this attitude is acceptable and based on this, the theory of full compensation of damage is accepted. In this article, collecting information is done using the library method and going directly to the applied resources and obtained data have been studied using analytical-description method of loss compensation.

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)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0070.010
Scholarly communication0.0130.008
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.066
GPT teacher head0.325
Teacher spread0.259 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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