Assessing the OECD Countries’ Industry 4.0 Maturity from Sustainable Development Goals’ Perspective: An Integrated PCA and DEA Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Industry 4.0 (I4.0) technologies and relevant research initiatives have been at the focal point of sustainable industrial development initiatives. Adoption of these technologies require a maturity level to create sustainable economic, social, and environmental benefits to society. In this study, we investigated the I4.0 maturity in OECD countries. A two-phase methodology is proposed: principal component analysis (PCA) and data envelopment analysis (DEA). The main contribution of the study to the state-of-art is a statistically reliable analytical framework which yields I4.0 maturity score from relevant United Nations Sustainable Development Goals’ perspectives. Results indicate that the proposed two-phase method significantly reduces the potential multi-collinearity impacts on I4.0 maturity performance. Moreover, USA, Sweden, Finland, and Switzerland were found to the on the efficiency frontier in terms of I4.0 maturity whereas Turkey, Chile, Latvia, and Mexico were found to be in the lowest ranks which need substantial policy implementation to increase their digitalization efforts.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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