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Record W4363672213 · doi:10.18280/ijsdp.180328

The Measurement of Public Policy Assessment of North Sumatra Province, Indonesia

2023· article· en· W4363672213 on OpenAlexvenueno aff
Muhammad Arifin Nasution, Nurman Achmad

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
FundersUniversitas Sumatera Utara
KeywordsGeographyEnvironmental planningEnvironmental protection

Abstract

fetched live from OpenAlex

This study attempts to explore the measurement model of public policy assessment of North Sumatra province, Indonesia.The research method employed in this study is a mixed-method of qualitative and quantitative approaches.The sample of this study is totaling to 100 participants.In data collection, questionnaires containing closed and semi-closed questions were used.Meanwhile, for qualitative data, interview was done to support quantitative data.From the analysis, it can be concluded that the purpose of evaluating the results of regional development plans is to ensure that regional development achievements are in line with established performance indicators.The regional apparatus planning agency of North Sumatra Province has only used budget realization as a metric for evaluating development planning thus far.Due to a lack of supporting data and qualified human resources in each regional apparatus organization, researchers discovered that regional apparatus organizations need help determining program and activity performance indicators.Several indicators, such as effectivity, adequacy, equity, responsivity, and accuracy must be developed in order to evaluate the success of a policy.In general, the inhabitants of North Sumatra believe that the development planning performance targets in North Sumatra are still low and have had little impact on the community welfare.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.045
GPT teacher head0.261
Teacher spread0.216 · 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
Published2023
Admission routes1
Has abstractyes

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