Prospects For the Re-Industrialization of Developed Economies (USA, Canada and Australia)
Why this work is in the frame
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Bibliographic record
Abstract
The article looks into the most important aspects of the world economy reindustrialization, examines strategies for reindustrialization of the USA, Canada, and Australia. The correlation between the world trend, namely the transition to the digital economy, and the process of reindustrialization within the framework of the Fourth Industrial Revolution is considered. On the basis of comparison and analysis of expert evaluations, statistical data by sectors of industrial production of the USA and Canada it is shown that the absolute advantage of Canada when carrying out the re-industrialization of the economy is skilled labor, specialists with secondary education. The study confirms the fact that amid the reindustrialization on the verge of the Fourth industrial revolution, the availability of skilled labor is a necessary condition for the competitiveness of the state. The Russian Federation faces the situation when conducting the re-industrialization is complicated by adverse international economic and political environment (policy of sanctions against Russia). It is revealed that for the Russian Federation the reindustrialization of the economy shall combine the active modernization of the existing production capacity, while shaping new industries on the basis of technologies of the sixth technology wave. The comparative analysis outlined that under the circumstances the drivers of the new industrialization should be science-based industries, with the latest technologies and the largest number of highly skilled personnel concentrating there.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it