Bibliographic record
Abstract
Based on data from official American statistics, the issue of wages in the United States of America manufacturing industry has been considered. This study is an important area of study of modern social and economic problems of the United States. Manufacturing plays an important role in the economy of the US, because it creates a material basis for all other industries. The trends and problems in this area have been revealed in the article. For a comprehensive analysis a systematic approach, economic-statistical and logical research methods have been used in the paper. A comprehensive study of wages in the most important sectors of the national economy has been carried out, based on data from the Bureau of Labor Statistics of the US Department of Labor. Separate attention has been paid to the category of “production workers”, whose share is about 70%. The statistical data on the average annual wage of production workers by industry according to the NAICS have been adduced. The significance of the manufacturing industry in creating, maintaining and returning jobs for the US economy has been shown.The difference in wages depending on the level of education, work experience and profession has been analyzed. The data on the highest paid industrial professions have been adduced. The uneven distribution of the manufacturing industry by states has been shown. It has been noted, that the reduction in the coverage of the trade union movement of American workers is another factor, affecting the level of wages. The correlation between production volume and Gini Coefficient in the USA in the period from 1947 to 2014 has been presented in the article. It has been noticed, that the growth of inequality in the US income and the decline of the manufacturing industry are interrelated.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".