Reindustrialization: A Challenge to the Economy in the First Quarter of the Twenty-First Century
Bibliographic record
Abstract
Abstract The weakening EU and US economies in the aftermath of the global crisis of 2007 need an impulse to act for the improvement of their condition. The analysis of the history of the GDP of selected world economies suggests that a remedy for it may be the strengthening of the industrial sector. By strengthening, we mean its growth, that is, building and developing manufacturing plants. Large multinationals have generally been relocating their production to China, where labor costs have traditionally been a couple of times lower than in the US or the EU. However, over the past years, the pay gap between the US and China has narrowed, and transport prices have gone up. These are the reasons why numerous large American companies decided to transfer part of their business processes back to the homeland. Also, the EU has been taking account of the benefits of a stable industry. Therefore, it has launched the strategy of “European industry rebirth” that entails a growth of the industry’s share in the GDP up to the level of 20%. In order for EU countries to be able to attain it, the paper raises the issue of the Industrie 4.0 methodology, premises and guidelines may, to a large extent, contribute to success. The paper also takes an in-depth look at Industrie 4.0 and discusses its pros and cons. We attempt to provide an answer to the question of whether Industrie 4.0 may be a tool for reindustrialization.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".