MétaCan
Menu
← Back to cohort
Record W2296093784

The Plight and Solution Concerning Hearing and Adopting Lawyer's Defense

2016· article· en· W2296093784 on OpenAlexvenueno aff
Wang Xiao-hong

Bibliographic record

VenueCanadian social science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
Fundersnot available
KeywordsFormalityLawActive listeningOrder (exchange)Political scienceDefense attorneySelf defensePsychologyBusiness
DOInot available

Abstract

fetched live from OpenAlex

The revised Criminal Procedure Law strengthens the participation of defense lawyer in criminal proceedings, clarifies that during the stages of investigation, arrest, before conclusion of investigation, prosecution, pretrial conference, trial and death penalty review defense lawyer’s views should be heard in order to protect human rights, avoiding making wrong judgments and build an equal criminal procedural structure of prosecution and defense. For the phenomena that formality has been gone through in listening to defense lawyer’s views and it is difficult to adopt lawyer’s rational defense in judicial practice, public security officers and judicial officers should change the idea of “underestimating defense”, give defense lawyer the right to the information, pay equal attention to lawyer’s substantive defense and procedural defense, achieve reasoning in judgment documents, give clear responses to defense opinions, establish appropriate support mechanisms to provide protection for defense lawyer to express opinions.

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)0.091
metaresearch head score (Gemma)0.145
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 score0.091
Threshold uncertainty score0.484

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.145
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0130.078
Scholarly communication0.0180.036
Open science0.0040.014
Research integrity0.0400.054
Insufficient payload (model declined to judge)0.0060.002

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 head0.050
GPT teacher head0.303
Teacher spread0.253 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian social science→Same topicCriminal Law and Evidence→French-language works237,207→