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Record W3184205355

The dynamics and regions features of the Ukrainian people employment in 90th of the XX century

2015· article· en· W3184205355 on OpenAlexaboutno aff
K.M. Nikolayets

Bibliographic record

VenueCherkasy University Bulletin: Historical Sciences · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor Market and Education
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianUnemploymentDemographic economicsWorkforcePopulationWageQuarter (Canadian coin)PovertyShadow (psychology)EconomicsGovernment (linguistics)Labour economicsGeographyPolitical scienceEconomic growthDemographySociology
DOInot available

Abstract

fetched live from OpenAlex

Main factors were defined, which affect on the dynamics and defined regions features of the Ukrainian people employment for 1990th. The effect of the spread the «shadow» economy on the development of the market of job in Ukraine was highlighted. Proved that the government policy on employment has led to the fact that on the beginning of XXI century unemployment was most common in Volyn, Zhytomyr, Mykolaiv, Sumy, Ternopil and Khmelnytsky regions where its level, taking into account the hidden exceeded the critical threshold rate of more than 2–3 times. Low cost remained the national workforce. At the beginning of XXI century nearly a quarter of the working population receiving incomes below the minimum wage. In most regions of Ukraine the level of average nominal wage of employees for a long period did not exceed the poverty line.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.031
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.186
Teacher spread0.164 · 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
Published2015
Admission routes1
Has abstractyes

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