Bibliographic record
Abstract
8.1 Italy has managed, in spite of the odds, to become one of the world's richest and economically most advanced countries. However, until recently, the literature on this remarkable achievement has failed to do it justice. Enquiry was distorted by ideology, vague theorizing, and, at best, modest attempts at empirical analysis. The situation, thanks to the contributions of several well-trained Italian economic historians and a number of foreign scholars, has improved dramatically in recent years. Once almost moribund, the field is now alive and well and loaded with opportunities for exciting and important research. We would encourage you to brush up on your Italian and join us in this endeavour. In the meantime, there seems to be emerging from the controversies in the literature a new view of the nature of long-run economic development in Italy. It has the following components. The performance of the economy in the nineteenth century, especially during the century's last decade and, more particularly, in agriculture, was better than the ISTAT series would have us believe. Industry was, on the whole (and over the long run), more competitive than once assumed. Moreover, robust industrial districts composed of highly efficient and competitive small and medium-sized companies are not, as many thought, the product of special features of the post-WWII economy, but instead have roots buried deep in Italy's industrial past. The supply of capital to industry was more complex and the links between mixed banks and industrial growth more attenuated than many have maintained.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.222 | 0.076 |
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".