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Record W4376278618 · doi:10.46799/ijssr.v3i4.355

Collaborative Governance in an Effort to Reduce Stunting Rate in TTS District, NTT Province

2023· article· id· W4376278618 on OpenAlexaboutno aff
Jacoba Daud Niga

Bibliographic record

VenueInternational Journal of Social Service and Research · 2023
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceCorporate governanceHumanitiesManagementEconomics

Abstract

fetched live from OpenAlex


 
 
 
 Di Provinsi NTT,, jumlah kasus stunting mencapai 42,62% atau very high prevelance stunting sehingga perlu adanya proses tata kelola kolaborasi (collaborative governance) antar lembaga dalam proses penanganan stunting khususnya di Kabupaten TTS Provinsi NTT sehingga penulis menggunakan model teoritis collaboration processes: inside the black box dari Thomson dan Perry (2006) sebagai suatu kajian teoritis yang mampu menjawab permasalahan tingginya angka prevalensi stunting di Provinsi NTT. Metode yang digunakan dalam penelitian ini adalah metode deskriptif dengan pendekatan kualitatif dengan total jumlah berjumlah 76 orang yang ditetapkan secara purposive kemudian data primer dan data sekunder yang terkumpul dianalisis menggunakan teknik analisis data dari Creswell (2016). Hasil penelitian ditemukan bahwa collaborative governance dalam upaya penurunan angka stunting di Kabupaten TTS Provinsi NTT dianalisis menggunakan pendapat Thomson dan Perry (2006:24-28) dengan hasil temuan sebagai berikut; (1) pada dimensi tata pemerintahan ditemukan bahwa pengambilan keputusan bersama untuk penurunan angka stunting belum dilakukan (2) pada dimensi administrasi masih terbatas pada persoalan integrasi basis data dari tiap-tiap indikator dalam setiap tujuan yang belum lengkap, valid, dan reliabel, (3) pada dimensi otonomi ditemukan bahwa belum ada pemahaman komperhensif mengenai indikator angka stunting oleh pihak-pihak yang berkolaborasi (4) dimensi mutualitas belum efektif karena minimnya saling ketergantungan antar aktor secara kelembagaan serta (5) proses membangun norma sosial ditemukan bahwa belum terdapat kepercayaan dan timbal balik dalam koordinasi penurunan angka stunting di Kabupaten TSS Provinsi NTT.
 
 
 
 
 
 
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 In NTT Province, the number of stunting cases reached 42.62% or very high prevalence of stunting so that there is a need for a collaborative governance process between institutions in the process of handling stunting, especially in TTS District, NTT Province, so the author uses the theoretical model of collaboration processes: inside the black box from Thomson and Perry (2006) as a theoretical study that is able to answer the problem of high stunting prevalence in NTT Province. The method used in this study is a descriptive method with a qualitative approach with a total of 76 people determined purposively then the primary data and secondary data collected were analyzed using data analysis techniques from Creswell (2016). The results of the study found that collaborative governance in an effort to reduce stunting rates in TTS District, NTT Province, was analyzed using the opinions of Thomson and Perry (2006: 24-28) with the following findings; (1) in the governance dimension, it was found that joint decision making to reduce stunting rates had not been carried out, (2) in the administrative dimension it was still limited to the problem of database integration of each indicator in each goal that was not complete, valid, and reliable, (3) in the autonomy dimension, it was found that there was no comprehensive understanding of stunting rate indicators by collaborating parties, (4) the mutuality dimension was not effective due to the lack of mutual dependence between actors institutionally and (5) the process of building social norms found that there has been no trust and reciprocity in coordinating the reduction of stunting rates in TSS District, NTT Province.
 
 
 

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.463
Teacher spread0.407 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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