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Record W2788376814 · doi:10.32676/n.3.4

Analiza utjecaja oblika vlasništva na zaposlenost u Hrvatskoj

2018· article· en· W2788376814 on OpenAlexaff
Marija Beg, Domenika Mergl

Bibliographic record

VenueNotitia · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Development and Management Studies
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsContext (archaeology)Econometric analysisEconomicsBusinessEconomyDemographic economicsMacroeconomicsGeography

Abstract

fetched live from OpenAlex

In the public discourse of transition economies, as well as in scientific literature focused on their functioning, there is still an active debate on whether privatization should be encouraged in view of its effects on the economy. In the context of the effects of privatization on employment, this paper focuses on labor market developments in Croatia. The primary emphasis is on the analysis of employment by activity and ownership type. The main aim of the paper is to determine the impact of privatization, i.e. the growing share of private ownership, on employment in Croatia. The estimate is conducted using the OLS method in the EViews statistical package. The results of the econometric analysis indicate that the increase in the share of private property leads to an increase in employment. In view of the results obtained it is recommended to further reduce the high share of state ownership in the Republic of Croatia.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.002
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 head0.055
GPT teacher head0.227
Teacher spread0.172 · 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
Published2018
Admission routes1
Has abstractyes

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