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Record W4225680545 · doi:10.22495/cgobrv6i1p14

The impact of active labour policies on economic growth

2022· article· en· W4225680545 on OpenAlexaboutno aff
Donat Rexha, Besime Ziberi, Alban Hetemi, Eda Gorda

Bibliographic record

VenueCorporate Governance and Organizational Behavior Review · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceUnemploymentEconomicsOrder (exchange)Agency (philosophy)Unemployment rateDescriptive statisticsActive labour market policiesGross domestic productQuarter (Canadian coin)WorkfareLabour economicsEconomic growthWelfareSociologyFinanceStatistics

Abstract

fetched live from OpenAlex

This study aims to analyze the mechanisms of active labour market policy in the case of Kosovo and the impact on reducing the unemployment rate and increasing employment. This research is descriptive, analytic, and exploratory. The data used are secondary data in the quarter for the period 2016–2020, which are provided by the Kosovo Agency of Statistics. The study uses the OLS (ordinary least square) econometric model and Pearson correlation in order to assess the impact of unemployment and employment rate on Kosovo’s GDP (gross domestic product). The paper concludes that Kosovo has approved a large number of programs for the activation of the unemployed, mainly young people, but generally young university graduates are in a higher structure and rate in the composition of the unemployment rate. It is generally accepted that university graduates as workforce are the key driver of economic growth and development (Ziberi, Rexha, & Ukshini, 2021). This allows us to come up with further recommendations, such as the active policies in labour market in the case of Kosovo to be designed in the future based on a cost-benefit perspective and in order to measure their effectiveness

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.687
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.257
Teacher spread0.227 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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