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Komu służy prawo? Libertariańska koncepcja Alberta Jaya Nocka

2021· article· en· W3217627801 on OpenAlexaboutno aff
Olgierd Górecki

Bibliographic record

VenueStudia Iuridica Lublinensia · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicTheology and Canon Law Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDoctrinePositivismObject (grammar)Argument (complex analysis)LawState (computer science)EpistemologyLibertarianismElement (criminal law)Government (linguistics)Political scienceLaw and economicsSociologyPhilosophyMathematicsLinguistics

Abstract

fetched live from OpenAlex

Albert Jay Nock (1870–1945) was a prominent opinion journalist of the first half of the 20th century, considered a representative of the first generation of libertarianism. The article is aimed at finding an answer to the question: Whom – according to Nock – does law serve? A key element of the problem is the internal dichotomy of the concept of law, which not only can be seen through the prism of the positivist-legal paradigm, but also constitutes the pillar of the jusnaturalistic concept. To properly arrange the object of study, the thesis was used according to which in Nock’s doctrine the existence of radically different assessment of the nature of man and his individual goals from the nature of the functioning of the State allows us to demonstrate the dichotomy of two opposing legal orders that serve the welfare of different entities (the individual and the State). To systematize the argument, the concept of the individual and his relations with the State was first presented, and then the dichotomy of the government and the State was discussed, which ultimately finally allowed to analyze the relationship between natural law and positive law.

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)0.001
metaresearch head score (Gemma)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.005
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.218
Teacher spread0.200 · 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
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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