MétaCan
Menu
Back to cohort
Record W7117411695 · doi:10.56466/orkes/vol4.iss2.117

Human Resource Development and Civil Service (ASN) Competency Improvement as well as Government Governance within the Riau Province Regional Development Planning Agency (BAPPEDA) in 2024

2025· article· W7117411695 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Olahraga dan Kesehatan (ORKES) · 2025
Typearticle
Language
FieldSocial Sciences
TopicPublic Administration in Developing Nations
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCompetence (human resources)Human resourcesCivil serviceCorporate governanceAgency (philosophy)Regional developmentGovernment (linguistics)Civil societyGood governance

Abstract

fetched live from OpenAlex

Human resource development (HRD) and improving the competence of State Civil Apparatus are the keys to achieving effective and efficient governance. In BAPPEDA Riau Province, this effort is the main focus in facing regional development challenges. This study aims to analyze the strategy for developing State Civil Apparatus Human resources Development and its impact on government governance in 2024. The methods used in this study are literature studies and in-depth interviews with a number of officials at BAPPEDA Riau Province. The results show that planning, leadership support, educational cooperation and periodic evaluation in improving State Civil Apparatus competence through formal and informal training and education have a significant impact on the quality of public services and better decision-making In BAPPEDA Riau Province.

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.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.002
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 head0.022
GPT teacher head0.301
Teacher spread0.279 · 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