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Record W3163727515

ДАВЛАТ ХИЗМАТЧИЛАРИ ФАОЛИЯТИ САМАРАДОРЛИГИНИ БАҲОЛАШДА ХОРИЖИЙ МАМЛАКАТЛАР ТАЖРИБАСИ

2020· article· ru· W3163727515 on OpenAlexaboutno aff
Турахужаева Раъно Рустам кизи

Bibliographic record

VenueЖурнал Социальных Исследований · 2020
Typearticle
Languageru
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
Fundersnot available
KeywordsPublic sectorProductivityGovernment (linguistics)BusinessWork (physics)Private sectorPerformance indicatorPublic administrationPolitical scienceEconomic growthEconomicsMarketingEngineeringEconomy
DOInot available

Abstract

fetched live from OpenAlex

One of the priorities of any public policy is to increase the efficiency of public service, in particular, the effectiveness of public servants. In this regard, a lot of work has been done in our country, in particular, a number of tasks have been identified to develop important performance indicators necessary to evaluate the performance of employees of ministries, departments and other government agencies. This article presents the experience of developed countries, such as Germany, the USA, Great Britain, Canada and Russia, in assessing the effectiveness of public servants and using modern methods of motivating them in accordance with their productivity. In particular, in recent years, information has been provided on the system of performance indicators (KPI), which is widely used in foreign countries not only in the private sector, but also in the public sector, on the basic principles of the system, and the stages of implementation of the efficiency system.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0390.014

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 head0.201
GPT teacher head0.445
Teacher spread0.244 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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Same venueЖурнал Социальных ИсследованийSame topicPublic Policy and Administration ResearchFrench-language works237,207