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Record W3118046296 · doi:10.23977/aetp.2020.41020

Socio-psychological determinants of resource potential of civil servants

2020· article· en· W3118046296 on OpenAlexvenueno aff
Lesia Brych, Mykola Brych

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2020
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Teacher Development
Canadian institutionsnot available
Fundersnot available
KeywordsLocal governmentPublic administrationCivil serviceCivil servantsGovernment (linguistics)Resource (disambiguation)State (computer science)Public relationsCivil societyBusinessWork (physics)Identification (biology)Service (business)Political sciencePublic serviceEngineeringLawPoliticsMarketing

Abstract

fetched live from OpenAlex

The problem of socio-psychological resource potential of the individual in modern social systems is one of the global and key problems in the civil service. Professional development of human resources of public authorities in the process of civil service reform, identification of social factors that affect the professional development of personnel. According to the implementation of the Concept of reforming the system of professional training of civil servants, heads of local state administrations, their first deputies and deputies, local government officials and deputies of local councils, socio-psychological determinants of resource potential of public managers are considered as the main mechanism for practical efficiency. work of state authorities and local governments.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.041
GPT teacher head0.394
Teacher spread0.354 · 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
Published2020
Admission routes1
Has abstractyes

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