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Record W4392837679 · doi:10.26618/kjap.v9i2.10846

Collaborative Governance dalam Menciptakan Branding Kota Surakarta Sebagai Kota Festival

2023· article· en· W4392837679 on OpenAlexaff
Robi Suwarna

Bibliographic record

VenueKOLABORASI JURNAL ADMINISTRASI PUBLIK · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsTourismCorporate governancePromotion (chess)Collaborative governancePublic relationsPlan (archaeology)BusinessTheme (computing)SociologyPolitical scienceMarketingGeography

Abstract

fetched live from OpenAlex

Festival activities are carried out as part of tourism promotion to market Surakarta City so that it can compete with other cities in the world in attracting investment, attracting tourists, and so on. It is necessary to identify the urban development plans carried out by the City of Surakarta, describe the roles of various actors from various sectors in the successful implementation of festival activities in the City of Surakarta, and explain the governance shape used in the City of Surakarta. The method used is descriptive and explanatory research with a qualitative approach to describe the development plans for the City of Surakarta and describe the integrative framework in Collaborative Governance that occurs in organising festival activities. The result of this research is the development plan for Surakarta City is to carry out city branding as a Festival City with a specific theme, namely its culture and the success of its implementation is the result of the collaboration of various actors from various sectors with their respective roles which an integrative framework model for Collaborative Governance can describe. This study concluded that Collaborative Governance is a governance shape carried out in Surakarta's city development planning. Collaborative Governance is considered a very good thing that impacts the economic and social community and cultural preservation.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.006
Science and technology studies0.0030.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.324
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 teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

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