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Record W2788223979 · doi:10.3968/10075

Improving Public Security Administrative Mediation System in China

2018· article· en· W2788223979 on OpenAlexvenueno aff
JI Hong-bo

Bibliographic record

VenueCanadian social science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOmbudsman and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsMediationParty-directed mediationAlternative dispute resolutionTransformative mediationPublic relationsBusinessLaw and economicsOrder (exchange)ChinaPolitical sciencePower (physics)Public securityLawSociology

Abstract

fetched live from OpenAlex

Public security administrative mediation is an important part of Chinese administrative legal system. It plays an important role in maintaining social order and stability, and preventing and resolving social disputes. A positive attitude by public security authorities towards social disputes is very important, while if public security authorities use its power to lead mediation to a certain way, or force mediation onto the disputants, the impacts can be quite negative. Public security should use administrative mediation on a wide range of civil disputes, and regulate the use of mediation through the establishment of appropriate procedures and disclosure of information and process related to the mediation. The court should also strengthen its monitoring and oversight of administrative mediation. Rather than reviewing the specific disputes that were mediated, the court should focus on whether mediation was conducted under the free will of the disputants, whether mediation results reflect an agreement by the disputants, whether mediation was carried out according to legal procedures, and whether mediation results were detrimental to public interests or the lawful interest of others.

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)0.008
metaresearch head score (Gemma)0.008
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 score0.093
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0060.002
Scholarly communication0.0040.003
Open science0.0030.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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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