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Record W2773375824

The Legislative Protection of the Equal Employment Right of Individuals With Criminal Records Based on the International Human Rights Law

2017· article· en· W2773375824 on OpenAlexvenueno aff
Hui Shi

Bibliographic record

VenueCanadian social science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicImmigration Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureLawHuman rightsPolitical scienceChinaInternational human rights lawCriminal law
DOInot available

Abstract

fetched live from OpenAlex

Individuals with criminal records should be considered as vulnerable group in the labor market and desire special arrangements with the aim to fulfill their equal employment right. With a view of International Human Rights Law, this article illuminates the principles to protect and promote their equal employment right, analyzes the legislations related to the equal employment right of individuals with criminal records in China and proposes a potential path to improve the legislative protection of the equal employment right of individuals with criminal records complying with the requirements of International Human Rights Law.

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)0.013
metaresearch head score (Gemma)0.016
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 score0.078
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0070.013
Scholarly communication0.0060.004
Open science0.0020.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.043
GPT teacher head0.309
Teacher spread0.266 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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