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Record W4366293255 · doi:10.53024/7.1.49.2023

Próba porównania niektórych elementów instytucji przyczynienia się poszkodowanego w deliktowej odpowiedzialności za szkody na osobie w prawie polskim i w systemie common law / Attempt at a Comparative Analysis of Certain Elements of Contributory Negligence of the Injured in Tortious Liability for Personal Injury Under the Polish Law and in the Common Law System

2023· article· en· W4366293255 on OpenAlexaboutno aff
Tomasz Strugalski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLawInstitutionLegislationCommon lawLiabilityElement (criminal law)Political scienceContributory negligenceTortPersonal injury

Abstract

fetched live from OpenAlex

The article provides a comparative analysis of the approaches related to contributory negligence of the injured causing the occurrence or aggravation of the injury in the Polish law and in the common law system. The article discusses the development of this institution, differences and similarities with special emphasis on the latter as peculiar to the systems that evolved from such distant sources as well as rules according to which the occurrence and degree of contribution is established. The basis for deliberations is the legislation of England and Wales under which this institution developed, from a chronological perspective, for the first time. However, certain American, Canadian and Australian approaches have also been discussed against this background. The article addresses important issues, though rarely debated in the Polish legal literature, and constitutes an important source of knowledge for legal scholars providing professional insights and prompting both axiological and purely practical deliberations. em Key words: tortious liability, personal injury, comparative negligence, contributory negligence, the common law system

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.427
Teacher spread0.363 · 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.

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
Published2023
Admission routes1
Has abstractyes

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