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Record W2070748734 · doi:10.5539/jpl.v7n2p150

The Defects of Anti-corruption Literature since 1990 and Revelation of the Legal Awareness in China

2014· article· en· W2070748734 on OpenAlexvenueno aff
Ruihui Han

Bibliographic record

VenueJournal of Politics and Law · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsRevelationLanguage changeChinaProsperityTastePolitical scienceMeaning (existential)Subject (documents)LawAestheticsLaw and economicsSociologyPsychologyLiteratureEpistemologyPhilosophyArt

Abstract

fetched live from OpenAlex

Anti-corruption literature sprouted up and prospered in China ever since 1990s, and such literature has significant meaning for the governmental fight against corruption. Anti-corruption literature is very popular and has great influence in Chinese readers. However, the analysis of such kind of literature reveals the defects in artistic form and subject content despite the superficial stories of it cater the taste of the readers. The defects are caused by the traditional thought mode and habit, especially the consciousness of ruling by the upright officers and the officialdom thought. The cultural background of China facilitates the birth and prosperity of anti-corruption literature, but at the same time it results in the defects of anti-corruption literature. For almost both the readers and writers, the legal awareness is lacked of, and the lack of it is not only one factor of corruption in reality, but also one cause of the defects of the Chinese anti-corruption literature since 1990. From the defects one can get the revelation of the legal awareness in China.

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)0.010
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: Review · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.008
Science and technology studies0.0050.008
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.336
Teacher spread0.320 · 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
GenreReview

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
Published2014
Admission routes1
Has abstractyes

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