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Record W3087415877

Дослідження проблем стабілізації кадрів на підприємстві з використанням зарубіжного досвіду.

2013· article· uk· W3087415877 on OpenAlexaboutno aff
Natalia Eliseeva

Bibliographic record

VenueВісник Донецького національного університету. Серія В. Економіка і право · 2013
Typearticle
Languageuk
FieldEconomics, Econometrics and Finance
TopicLabor Market and Education
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityWork (physics)BusinessJob rotationTurnoverProduction (economics)Operations managementLabour economicsPublic relationsEconomicsManagementJob satisfactionPolitical scienceEconomic growthJob performanceEngineeringJob designMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

The article develops the problem of the staff stabilization at the enterprises of allocation and study of the main component of staff stabilization - rotation. Describes the main aspects of the staff rotation in the organization, and describes the causes of the problem, its consequences. Studied foreign experience about decision of firm’s staff rotation problems. Most Western firms classifies staff rotation as a negative phenomenon, because it leads to lower productivity and production volumes. This issue has been overlooked at enterprises of Germany, the USA, Canada, Japan, etc Determined that the high turnover of personnel is often: the lack of adequate planning, personnel, resulting in workers trying to enter into emergency mode with all the ensuing consequences; unbalanced system of motivation and stimulation of labour, lack of career and professional growth; low equipped working places, poor infrastructure, exhausting work, etc. Based on the identified reasons offered some recommendations to reduce the fluidity and retain personnel in modern conditions.

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 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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0030.005
Science and technology studies0.0020.001
Scholarly communication0.0040.005
Open science0.0050.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0590.084

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 head0.018
GPT teacher head0.181
Teacher spread0.163 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueВісник Донецького національного університету. Серія В. Економіка і правоSame topicLabor Market and EducationFrench-language works237,207