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Record W7113897461 · doi:10.62383/dialogika.v1i4.760

Dampak Perubahan Kebijakan Pengadaan Secara Elektronik melalui E-Katalog V5 Menjadi V6 bagi Penyedia di Kecamatan Balongbendo

2025· article· W7113897461 on OpenAlexaboutno aff

Bibliographic record

VenueDialogika Jurnal Penelitian Komunikasi dan Sosialisasi · 2025
Typearticle
Language
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementTransparency (behavior)E-procurementHuman resourcesQuarter (Canadian coin)Goods and services

Abstract

fetched live from OpenAlex

This study aims to analyze the implementation of procurement of goods and services through e-Catalog version 6 in Balongbendo District after the enactment of policy changes from the previous version (V5). In addition, this study also identifies impacts, obstacles, and strategies to increase system effectiveness. This study uses a qualitative descriptive method with a document study approach based on the Electronic Procurement of Goods/Services Implementation Report for the third quarter of 2025 as a secondary data source. The results of this study show that the implementation of the V6 e-Catalog has been relatively effective with a completion rate of 87.18% of a total of 39 procurement packages. However, obstacles were found in the form of human error (75%) and system bugs (25%) that caused some packages to be unable to be processed. The transition from version 5 to version 6 brings a positive impact in terms of increased transparency and efficiency, but it also poses new challenges in adapting to the latest version. This study confirms that the success of digital transformation of public procurement does not only depend on technological aspects, but also on human readiness and adaptive institutional governance.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.004
Science and technology studies0.0060.004
Scholarly communication0.0060.004
Open science0.0060.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.283
Teacher spread0.264 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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