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Record W4391936845 · doi:10.21787/jbp.15.2023.543-556

The Role of Economic Intelligence in Accelerating Welfare of Gorontalo Province

2023· article· en· W4391936845 on OpenAlexaff
Herie Saksono, Mahyudin Humalanggi, Nancy Noviana Lantapon, Ivana Butolo

Bibliographic record

VenueJurnal Bina Praja · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicLeadership, Behavior, and Decision-Making Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsWelfarePsychologyEconomicsMarket economy

Abstract

fetched live from OpenAlex

The Gorontalo Province region has the potential for significant resource wealth. However, the Gorontalo Provincial Government still faces problems and challenges when trying to improve the welfare of its people. One is the limited data/information that is accurate, up-to-date, and integrated. This data/information is important in planning and making appropriate decisions. Another problem is that the community's role in modern development based on economic intelligence in Gorontalo Province has not been optimal. This means that the public and even bureaucrats are still unfamiliar with "Economic Intelligence." As a result, economic intelligence has not been fully used in planning, implementing programs/activities, evaluating and monitoring, or government decision-making processes. This research aims to analyze the problems and challenges in modern development based on economic intelligence in Gorontalo Province. This research uses a qualitative-descriptive approach. This study found that the main problems in modern development based on economic intelligence in Gorontalo Province are: 1) The lack of integration of economic data/information from various government and private agencies; 2) Lack of human resources (HR) who are reliable and have competence as managers and/or analysts of scientific data; and 3) Lack of technological support in managing economic data/information. The results of data analysis also show that the role of society in modern development based on economic intelligence is still very low. A lack of public understanding of the importance of economic intelligence causes this. Therefore, it is recommended that the Gorontalo Provincial Government make: 1) efforts to integrate economic data/information from various government and private agencies; 2) increase human resource capacity in managing and analyzing scientific data; 3) provide technological support in managing economic data/information; and 4) increase public understanding of the importance of economic intelligence.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.114
GPT teacher head0.394
Teacher spread0.280 · 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
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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