Bibliographic record
Abstract
The article reveals the most important aspects of the problem of reindustrialization of the world economy, examines strategies for reindustrialization of the USA, Canada, and Australia.The relationship between the world trend - the transition to the digital economy - and the processes of reindustrialization within the framework of the Fourth Industrial Revolution is considered. The shifts in manufacturing in the US are prompted by the imposed restrictions in the form of an increase in the import tax, along with the introduction of tax energy incentives for domestic production. Undisputed advantage of Canada in carrying out the reindustrialization of the economy is a highly skilled labour force, specialists with secondary education. In conditions of reindustrialization on the eve of the Fourth Industrial Revolution, the availability of highly skilled labour is a necessary condition for the state's competitiveness. The Russian Federation is faced with the situation when reindustrialization is complicated by unfavourable external economicand foreign policy conjuncture (a policy of sanctions against Russia). For the Russian Federation,the reindustrialization of the economy should imply an extensive modernization of the existing production capacities, as well as the formation of new industries based on the use of technologies of the sixth technological order. In these conditions, the drivers of the new industrialization should be knowledge-intensive industries, in which the latest technologies and the largest number of highly skilled personnel are concentrated.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".