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Record W2074535315 · doi:10.5539/ass.v4n10p81

99% Normal Adjudication and 1% Supernormal Adjudication ---- Posner Paradigm and Construction of Chinese Scholar-Type Judge Mechanism

2009· article· en· W2074535315 on OpenAlexvenueno aff
Min Niu, Fang Chen

Bibliographic record

VenueAsian Social Science · 2009
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAdjudicationRationalityEquity (law)Mechanism (biology)Law and economicsOpenness to experienceLoyaltyLawChinaPolitical scienceSociologyEpistemologyPsychologyPhilosophySocial psychology

Abstract

fetched live from OpenAlex

Law of individual equity pursues for scholar-type judges who are endowed with radical talents and revolutionary spirits. To trigger out these judges’ natural advantages of mastering law theories and reform trends and overcome their shortcomings of ignoring equity of interests in criticizing legal system and seeking for radical reform are the key parts in the modernization of China legal system. “Double excellent law man” Posner’s paradigm shows the core of constructing a scholar-type judge mechanism, that is, to cultivate judges who possess characters of being modest, wary, and responsible, gifts of openness, wisdom, and unselfish, super ability of logic analysis, loyalty to rationality, sagacity and courage of grasping reform chances and making supernormal adjudications, and avoid the attitude of “results-oriented adjudication”.

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)0.027
metaresearch head score (Gemma)0.025
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: none
Teacher disagreement score0.027
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0050.021
Scholarly communication0.0070.006
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.002

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 head0.382
Teacher spread0.360 · 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
Published2009
Admission routes1
Has abstractyes

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