Searching for “rare diamonds”? Industrial districts and innovation in Spain and Italy
Bibliographic record
Abstract
Purpose This paper aims to test the existence of the so-called industrial district effect on innovation (iMID effect) in Spain and Italy and to compare the intensity of this effect between both countries. There is previous evidence of this effect for Spain, although, to the best of the authors’ knowledge, it has never been measured for Italy. Design/methodology/approach Innovation intensity by local production system is measured using patents per million employees and analysed using the mean, the median, 3D maps and statistical tests. Findings Industrial districts generate between a third and a quarter of all technological innovations in Spain and Italy. The evidence about the district effect in innovation in Spain is consistent with previous studies. The novelty is that there is also evidence of this effect for Italy and its intensity is higher than for Spain. Almost one-half of the industrial districts fit in the most innovative quartile of local production systems, and they are located in the most innovative part of each country. Research limitations/implications Limitations of this study include minor database issues. Implications include new focus on the general relevance of industrial districts as highly innovative local production systems and top innovators. Practical implications Reorientation of territorial and innovation policies. Social implications Effect on development and well-being through technical progress. Originality/value This article provides, for the first time, to the best of the authors’ knowledge, a measurement of the industrial district effect on innovation in Italy. The paper compares the results between Spain and Italy and allows for generalization of previous evidence, concluding that highly innovative industrial districts are not “rare diamonds”, revealing as an alternative and an extraordinarily powerful place-based innovation model.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".