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Record W1833497418

Study on the Adjustment Procedures of Administrative Division in China: From The Perspective of Text Analysis

2015· article· en· W1833497418 on OpenAlexvenueno aff
Liyan Hu

Bibliographic record

VenueCanadian social science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Law, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsAdministrative lawLegislationAdministrative divisionPlenary sessionLegislatureOrder (exchange)Element (criminal law)Public administrationChinaPolitical scienceQuality (philosophy)LawLaw and economicsBusinessEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Driven by new urbanization strategy, the administrative division becomes the hot point of public view. However, due to the absence of the legal procedures, in the process of administrative division adjustment, some local people’s appeals have not been responded, which sometimes led to group incidents against social stability. In order to meet the requirements to comprehensively promote the rule of law, the 3th plenary session of 18th CPC Central Committee proposed strict procedures of administrative division adjustment programs, and the 4th Plenary Session proposed to improve the decision-making mechanism in accordance with the law. After carefully combing laws and regulations promulgated by the central and local governments at all levels or Ministry of Civil Affairs, we found that there are many deficiencies in the legal effect, legislative quality, procedures specification, follow-up mechanism and so on. This paper, from the perspective of text analysis, and based on careful reviews, analysis and assessment of the current procedural provisions, gives suggestion of improving the adjustment procedure of administrative division, and provides reference for the legislation of administrative division in the future in the connection of administrative law theory and administrative procedure 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.390
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2015
Admission routes1
Has abstractyes

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