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Record W2924850048 · doi:10.35586/.v4i1.124

TELAAH TERHADAP PRESIDENTIAL THRESHOLD DALAM PEMILU SERENTAK 2019

2017· article· en· W2924850048 on OpenAlexaff
Lutfil Ansori

Bibliographic record

VenueJurnal Yuridis · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndonesian Election Politics and Participation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPresidential systemPolitical scienceLegislatureConstitutionLegislatorRelation (database)Law and economicsLawComputer scienceLegislationEconomicsPolitics

Abstract

fetched live from OpenAlex

This paper aims to examine the presidential threshold in relation to the simultaneous general elections 2019. After the decision of the Constitutional Court Number 14/PUU-XI/ 2013 which mandates the general election simultaneously raises the pros and cons of setting the presidential threshold. In the constitutional perspective, using or not using the presidential threshold is not contrary to the constitution, because the presidential threshold is an open legal policy of the legislator. The legislators need to rethink the provisions of the presidential threshold especially in relation to the simultaneous elections, taking into account the advantages and disadvantages of applying or abolishing the presidential threshold, in order for the purpose of strengthening the presidential system to be achieved. The existence of simultaneous general elections has substantially eliminated the provisions of the presidential threshold, so the threshold requirement to nominate the President and Vice President becomes irrelevant. However, if the legislators demand presidential threshold, the middle path that can be selected is to apply the presidential threshold by using the legislative election 2014 with a record of institutionalizing the coalition.

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)0.001
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.037
GPT teacher head0.358
Teacher spread0.321 · 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
GenreOther

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

Citations37
Published2017
Admission routes1
Has abstractyes

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