Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".