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Record W3114183835 · doi:10.6000/1929-4409.2020.09.299

Quadruple Helix Model on Creative Economy Development in Bandung Regency

2022· article· en· W3114183835 on OpenAlexvenueno aff
Ferry Hadiyanto, Bayu Kharisma, Sutyastie Soemitro Remi, Ardi Apriliadi

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
FundersUniversitas Padjadjaran
KeywordsGeneral partnershipCreative economyGovernment (linguistics)SWOT analysisWork (physics)PhenomenonCreative industriesFunction (biology)EconomyBusinessEconomic growthPublic relationsMarketingEconomicsPolitical scienceEngineeringCreativityFinanceLawMechanical engineering

Abstract

fetched live from OpenAlex

This paper uses the quadruple helix model and creative economy as research variables. This study is conducted on the creative sector in Bandung Regency. The research process is mainly executed through focus group discussion (FGD) with all essential parties and interviews with stakeholders in regional work units. The FGD and interview procedure will generate accountable information related to (1) Potential map for the creative economy; (2) SWOT analysis and available partnership programs for the development of the creative economy; (3) a description of cooperation system between related parties in Quadruple helix; (4) Conception of a government partnership program with creative communities through program activities. This study's significant result indicates that the role of government in Quadruple helix cooperation is 60 percent in 2016 and decreasing to 10 percent in 2019. The reduction of the governmental function is also accompanied by the increasingly dominant role of creative entrepreneurs each year, reaching 55 percent in 2019. This phenomenon shows that the development of the creative economy is becoming more independent every year.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.067
GPT teacher head0.275
Teacher spread0.208 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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