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ON LEGAL DEFECTS AND LEGAL DEFECTOLOGY

2018· article· ru· W2946685998 on OpenAlexfundno aff
Igor P. Kozhokar

Bibliographic record

VenueВестник Пермского университета Юридические науки · 2018
Typearticle
Languageru
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsPolitical scienceBusinessMedicine

Abstract

fetched live from OpenAlex

Introduction: this paper deals with defects in the regulatory part of the legal regulation mechanism as deficiencies in regulatory and legal matter.Commitment to high quality of legal norms and regulatory acts is a prerequisite for the effectiveness of law and the main requir ement to lawmaking.Creating legal uncertainty and being a fertile field for the emergence of many negative legal phenomena, regulatory defects should become an independent subject matter of a new scientific area of legal defectology.Purpose: to formulate the concept 'regulatory legal defect' t aking into account the complexity of defectiveness as a legal phenomenon and its relationship with many other legal categories; to justify the importance of its independent research within the theory of law.Methods: the research is based on the general scientific dialectic approach to cognition of legal defects, which allows for considering this phenomenon in its formation and development as well as in conjunction with related phenomena; other methods applied in course of research include methods of formal logic and specific scientific methods of studying legal reality (formal dogmatic, hermeneutical approach, legal modeling).Results: understanding of regulatory legal defects is in dialectical unity with the concepts 'quality of law' and 'effectiveness of law'.A regulatory legal flaw can be recognized as a defect when it violates the quality standard of law and adversely affects the performance of a legal regulator.Conclusions: the effectiveness of law is influenced by both non-legal factors (political, managerial, financial, social, psychological, ideological) and some legal deficiencies related to defects (restrictions, obstacles, administrative barriers, legislative imbalance, neutralization of law).Regulatory legal defects, mutually determined and interacting with other legal flaws, have their own content and their own conceptual line, reflecting certain shortcomings of the content, form and structure of legal rules and regulatory legal acts.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0030.052
Scholarly communication0.0050.011
Open science0.0020.006
Research integrity0.0030.005
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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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