The diffusion of industrial robots in Europe: regional or country effect?
Bibliographic record
Abstract
Abstract The paper investigates whether the penetration of advanced manufacturing technologies can be better explained at the regional or national level. If regional effects prevail, policy actions would focus on local investments, while if country effects make regional covariates redundant, they should be redirected to more structural reform of the national systems of innovation. In this respect, the contribution is 2-fold. First, data on acquisitions of industrial robots in the five largest European economies are rescaled at regional levels to draw a clear picture of winners and losers in the robotics race after the 2008 financial crisis. Second, we explain differential of growth rates in robot adoption with (1) traditional measures of industrial variety, (2) an unsupervised machine learning approach classifying a region’s industry profile (3) usual determinants of innovation and, thereafter test the robustness of the results when country effects are added. As the main result, we highlight a process of regional convergence in which country-fixed effects hold greater explanatory power, although related variety and the number of skilled people are statistically significant regional explanatory factors. We do not discover a specific industry mix associated with the rise of adoption, but we highlight the one associated with its decline.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".