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Record W4231957532 · doi:10.52062/keuda.v5i1.1216

Analisis Faktor-Faktor Yang Mempengaruhi Pengelolaan Aset di Institut Pemerintahan Dalam Negeri Kampus Papua

2020· article· en· W4231957532 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

VenueKEUDA (Jurnal Kajian Ekonomi dan Keuangan Daerah) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusinessAuditAsset (computer security)Asset managementTrustworthinessAccountingSample (material)Human resourcesBusiness administrationFinanceManagementEconomicsPsychologyComputer science

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the assets management in the Papua Campus of Institut Pemerintahan Dalam Negeri (IPDN). The research also wants to reveal how the influence of Legal Audit, Human Resources and Leadership Commitments on Optimizing Asset Management. We surveyed on this Campus by selecting a sample of 30 respondents. We empirically tested our hypothesis using Multiple Regression Analysis. The results show that the assets management on this Campus is proper and running under the appropriate statutes. Still, there are needs for more advance and thought in the assets administration, utilization and supervision. Legal audit proved to have a positive but not significant effect on asset management. It means that the audit does not guarantee asset optimization. Human resources and leadership commitments have a positive and significant impact on asset management, reflects that if human resources and leadership commitments are getting more robust, asset management will also be more trustworthy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.674
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.214
Teacher spread0.192 · 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