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Record W4361975091 · doi:10.55365/1923.x2023.21.33

The Impact of Education in Economy. The Case of Albania

2023· article· en· W4361975091 on OpenAlexvenueno aff
Alba Ramallari, Entela Velaj

Bibliographic record

VenueReview of Economics and Finance · 2023
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryEmigrationProduction (economics)EconomicsPopulationEconomic growthDevelopment economicsLabour economicsDemographic economicsMarket economyPolitical scienceSociologyMacroeconomics

Abstract

fetched live from OpenAlex

As an economist and researcher of macroeconomic models for economic growth, we give importance to labor efficiency to increase the level of production.Efficiency is directly related to education, qualification, specialization, etc.We give importance to the educational part, as we also connect it with the salary and the advantages that education gives to a country as a public good.Being a country in transition makes us appreciate it more and as individuals we invest in it, but generation after generation have tried to get an education and leave the country.This is the reason that education in our country gives the expected results.However, education is also related to the culture received from generation to generation.The purpose of this study is to discover the relationship between the education of the population and the economic growth of the country.The result of the study was surprising.Education in Albania does not make a positive contribution to economic growth.On the contrary, it gives a negative impact.Albania is facing an increasing emigration of educated and professional individuals.This has a negative impact on economic growth as the country has spent on their education while the host country benefits from the contribution.Give a man a fish and he will eat for a day.Teach a man to fish and he will eat for a lifetime."-Laozi(老子) 1 .

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.262
Teacher spread0.250 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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