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Record W2782655575 · doi:10.5539/jpl.v11n1p17

Protecting Human Rights and Constitutional Law in Bicameral Systems

2018· article· en· W2782655575 on OpenAlexvenueno aff
Mohammadreza Baharestanfar, Seyed Mohammad Hashemi

Bibliographic record

VenueJournal of Politics and Law · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Analysis in Indonesia
Canadian institutionsnot available
Fundersnot available
KeywordsLawHuman rightsPolitical scienceConstitutional lawLegislatureNormativeDemocracyPolitics

Abstract

fetched live from OpenAlex

Background and objective: The second legislative chamber has played different roles and functions since its formation in ancient Rome and Greece. The philosophy behind the presence of this chamber (either in Federal systems or unitary systems) was a matter of controversy between its proponents and critics. There are more than 78 countries with two legislative chambers in the world. Protecting constitutional law and human rights are two notable functions of the second chambers. Research method: This paper used the descriptive–analytical method. The methods used by some second chambers are discussed as an example. Results (findings): how a second chamber can be considered as the scout of constitutional law and protector of human rights with regard to their normative behavior depends on the structure of the constitutional law. The role of the second chamber in protecting constitutional law is manifested in several forms: coinciding the bills and laws with constitutional law, amending and revising the constitutional law, vetoing or suspending bill a general appointments. Second chambers have several solutions including establishing the human rights committee with various duties. Therefore, these two criteria seem to be useful in order to measure the extent of democracy. Conclusion: A powerful second chambers are needed to make decisions, have the authority to amend the laws, and to have effect on the politics to realize human rights and protection of Constitutional law. It can also act as a human rights watch with regard to the nature of the norms of human rights.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.014
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.334
Teacher spread0.306 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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