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Record W3035621892 · doi:10.30525/2661-5169/2020-1-5

WAGE PAYMENTS IN THE WORLD

2020· article· en· W3035621892 on OpenAlex
Anzhelika Mashevska

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGreen Blue and Digital Economy Journal · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture Market Analysis Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryWageEarningsUkrainianChinaPopulationPaymentImmigrationDemographic economicsPolitical scienceLabour economicsEconomicsBusinessSociologyLawAccountingDemographyFinance

Abstract

fetched live from OpenAlex

The purpose of the article is to analyze earnings in different countries of the world. The wages of the population of different countries are analyzed: the USA, Canada, the former Soviet Union countries, the rating of 30 states-leaders on average salary (gross) is made. It is proved that, in addition to national statistical institutions, international organizations are also engaged in the compilation of wage ratings. Their statistical surveys are highly reliable: when calculating the average wage, salaries of employees are taken into account, emphasizing their qualifications and work experience, without taking into account businesspersons, private or individual entrepreneurs, pensioners, assisted persons and others. Method. According to the ratings, the list of the most sought after and highly profitable professions is constantly changing. The labor market is out of place, and before the prestigious specialties cease to be relevant, and their place is occupied by new ones, the demand of representatives of a profession also depends on the region. What has become of further development is that in recent years many popular and unusual professions have appeared in the countries of the Far East: Japan, Korea, China, Taiwan, Hong Kong and others. For example, many Ukrainian citizens teach English as a "native" language in China. It is important for the Chinese that the teacher be European, and the demand for language courses is enormous (especially in the province). Results. For those citizens who have pronounced Caucasian features, they have blond hair, fair complexion, and eyes that are beautiful and young, with even greater opportunities to earn money, the trend for the European appearance in China, Korea and Japan is huge. Value/originality. According to the analysis of the countries with the highest average salary level, 20 positions belong to the European countries, 2 are from America and Oceania and 6 are Asian. The important products and services can have a serious impact on cost of living, with 100 USD being of different weight in Japan and in Ukraine. Therefore, the inflationary processes that enter the economy significantly affect the level of wages of people, which in turn affects the standard of living of the population.

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.

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 score0.675
Threshold uncertainty score0.318

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)0.0000.000

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 head0.015
GPT teacher head0.186
Teacher spread0.171 · 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