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Record W3121345134 · doi:10.5539/ass.v17n2p83

Study on the System of Penalty Substitution in Taiwan, China—Based on 494 Commutation Judicial Documents

2021· article· en· W3121345134 on OpenAlexvenueno aff
Weiwei Du

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Studies and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureCommutationMainland ChinaPenalty methodStatutory lawSubstitution (logic)ChinaLegislationFunction (biology)Goldilocks principlePunishment (psychology)Term (time)LawValue (mathematics)Law and economicsComputer scienceEconomicsPolitical scienceMathematicsMathematical optimizationEngineeringPsychologySocial psychology

Abstract

fetched live from OpenAlex

Although the penalty substitution system has not been established in mainland China, there is no lack of relevant discussion. Some scholars suggest that we should learn from the introduction of a commutation system to alleviate the disadvantages of short-term freedom penalty and the difficulty of fine penalty implementation; other scholars discuss the function and value of penalty substitution. In mainland China, short-term free penalty and fine penalty are statutory punishments for many crimes, and if the penalty replacement can be realized within a reasonable range, it can also alleviate the disadvantages of the execution of the penalty. The judicial system of Taiwan is rich in judicial practice and has a long time to revise. Based on legislative data and 494 judicial documents related to commutation in Taiwan, China, a comprehensive review of the penalty substitution system in Taiwan will help to explore the role and limitations of penalty substitution as an alternative to punishment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.345
Teacher spread0.317 · 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.

Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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