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Record W4311490205 · doi:10.58487/akrabjuara.v7i4.1962

PENGARUH GAYA KEPEMIMPINAN KAPOLS MOTIVASI DAN KINERJA ANGGOTA POLSEK JATIASIH

2022· article· en· W4311490205 on OpenAlexaff
Roydawaty Bunga S

Bibliographic record

VenueAkrab Juara Jurnal Ilmu-ilmu Sosial · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsLeadership stylePsychologyGovernment (linguistics)EnthusiasmWork performanceOrganizational performanceBusiness administrationManagementSocial psychologyBusinessMarketingEconomics

Abstract

fetched live from OpenAlex

Every organization needs a leader who can provide enthusiasm, guidance, direction on the performance of its employees/employees in achieving organizational goals. Leadership style and motivation have an important role in implementing performance for Village Government employees in the Lumajang District, Lumajang Regency, that there is an influence of leadership style and motivation simultaneously/partially on the performance of Kelurahan Government employees in Lumajang District, Lumajang Regency, the leadership style of the Kapolsek dominantly influences the performance of members of the Jatiasih Police, this shows the importance of leadership style and motivation to improve employee performance. If the leadership style and giving motivation are not paid attention to, then there will be a decrease in work performance/achievement. After conducting an evaluation, it can be concluded that there is a need to increase attention and policies towards members of the Jatiasih Polsek regarding performance, the problem of standard time which can still be said to be lacking and must design motivation for good members to be able to achieve organizational goals

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.004

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 head0.027
GPT teacher head0.302
Teacher spread0.275 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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