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Record W2757661059 · doi:10.5539/jpl.v10n5p8

Principles of Civil Liability Arising from Bodily Injuries in Iranian and English Law

2017· article· en· W2757661059 on OpenAlexvenueno aff
Majid Sarbazian, Mehdi Sokhanvar, Abedin Rahimi Pordanjani

Bibliographic record

VenueJournal of Politics and Law · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLegal liabilityLawJurisprudenceLiabilityDelictAction (physics)Civil law (Civil law)Strict liabilitySubject (documents)Political sciencePrivate lawComparative lawPublic lawBlack letter lawComputer science

Abstract

fetched live from OpenAlex

Explaining the principles of civil liability in cases that someone injured bodily is now one of the most important issues of law. In fact, in these cases, the question is why someone should compensate other damage? While has not been violated of the contract. If doing the action is allowed, and the subject has the legal authority to do it or fault or bad intention does not occur from him, can be imagined again a responsibility for him? In this regard, civil and criminal liability separate from each other and each is analyzed separately. Foundations of theoretical, practical, legal and in addition to these, in our rights jurisprudence foundations have been added to the former cases. Each of these has sub sets, and each tried to explain that when someone injured bodily the other, why and how to compensate? Who should compensate? To compensate, what must be proved? And of course, in similar cases, results are obtained that each has minor and sometimes major differences. Now, the theoretical foundations (fault, risk, etc.) are accepted and analyzed in Iranian law and in English law, but absolutely none of them have gone towards one of the comments.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.064
GPT teacher head0.409
Teacher spread0.344 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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