Impacts of Industry 4.0 in developed countries & BRICs
Bibliographic record
Abstract
Abstract This paper aims to compare I4.0 manufacturing countries, providing an overview of the most relevant issues related to their patent and exportation figures. The countries researched are Germany, Brazil, China, South Korea, the US, India, Japan, and Russia. A Literature analysis was applied in Scopus and Web of Science publications (2012–2022), which include BRICs countries. Patent records related to high-tech products Patentscope of the World Intellectual Property Organization and the list of exported products in TradeMap were also searched to identify and analyze the contribution of each country’s policies. Findings present the positive effects of I4.0 policies in the industrial context of most countries, as evidenced by an increase in exportation and patents related to manufacturing high-tech goods since the policies were implemented after 2014. Patent registration evolution refers to the evolution of high-technology goods in the last nine years. This study also indicates the motivations of each country for implementing Industry 4.0. A country comparison of the durations, objectives, available funding, areas for action, focused manufacturing sectors, and prioritized technologies of these public policies supports researchers, managers, and policymakers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".