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Record W2904852339 · doi:10.3968/10687

Reflection and Improvement of Litigation Relief in China's Administrative Contract Dispute Resolution

2018· article· en· W2904852339 on OpenAlexvenueno aff
Qian Long

Bibliographic record

VenueCanadian social science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicConflict of Laws and Jurisdiction
Canadian institutionsnot available
Fundersnot available
KeywordsNormativeVaguenessScope (computer science)Interpretation (philosophy)Dispute resolutionAdministrative lawJudicial interpretationLaw and economicsLawBusinessAmbiguityPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

The clauses related to the administrative contracts in the new Administrative Litigation Law and the judicial interpretation about the administrative contracts in Interpretation of Applicability together constitute the normative basis of the current case hearing of the administrative contracts. However, in the current practice of judicial trials of administrative contracts, the intrinsic defects of the normative basis are gradually becoming more apparent. These defects specifically include the vagueness of the scope of case accepting, the unclarity of the normative application rules, and low operability etc. This article intends to reflect on the above defects. The author argues that it is necessary to define the scope of case accepting of administrative contracts based on positive and negative criteria. Within the subset of administrative litigation - the administrative contracts, discriminating the characteristics of differences disputes and making the disputes applicable to different normative application rules.

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)0.013
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0060.003
Scholarly communication0.0070.004
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.337
Teacher spread0.315 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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