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Record W2266580488 · doi:10.18196/jgp.2012.0006

PROMOTING DEMOCRATIZATION AND GOOD GOVERNANCE: RIFKA ANNISA WOMEN CRISIS CENTER YOGYAKARTA

2012· article· en· W2266580488 on OpenAlexaff
Lalu Fadlurrahman

Bibliographic record

VenueJurnal Studi Pemerintahan · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Labor and Employment Law
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsDemocratizationCorporate governanceDecentralizationPoliticsCivil societyWork (physics)Government (linguistics)Public administrationPolitical scienceOrder (exchange)Good governancePublic relationsBusinessDemocracyManagementEconomicsEngineeringLaw

Abstract

fetched live from OpenAlex

Since the reformation era in 1998, the number of Non-Governmental Organizations in Indonesia is exploding and their impact on Governance growing. Decentralization Policy has supported this trend with its goal to enhance development in the regions, requiring more active involvement of the people in formulating their interests. Organizations can play an important role in the regions as a bridge between the people and the government. In order to strengthen that role, Non-Governmental Organizations must have a solid organisational fundament and political strategies to get actively involved in the policy making process. An additional stronger legal fundament for such an active role could force the government agencies to include NGOs in the part a policy process. This paper delivers a case study on the actual performance of a local Non-Governmental Organization in the field of Gender Politics and Good Governance. Organisational set up and results of attempts to play an active role in articulating the interests of people it represents are reviewed and lead to suggestions for improvement of performance, that can work as guideline for other NGOs in Indonesia who want to contribute to local Governance. Keywords: Civil Society, Good Governance, NGOs, Rifka Annisa Women Crisis Centre

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.299
Teacher spread0.284 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2012
Admission routes1
Has abstractyes

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