Bibliographic record
Abstract
2.1 After a brief burst of research on the economic causes of the Risorgimento (the political process of unification) in the 1950s and early 1960s, in large part stimulated by the centenary of Italian unification in 1961, historians lost interest in the economic history of pre-unification Italy. Thus, recent reviews of the literature by Pescosolido (1998) and Crepas (1999) have almost nothing new to say about the period 1815–1860. This is most unfortunate because, in spite of tantalizing suggestions by Cafagna (1989) and Bonelli (1979) – see chapter 3 – that modern economic growth in Italy probably predated unification, these and other issues have received very little attention. There are, moreover, underutilized sources of information on the period. In short, then, the pre-unification period is still awaiting the renaissance in economic history research experienced by other periods. The payoff to such renewed attention could be substantial. Foreign trade statistics are the most reliable and by far the largest set of data we have on the real economy before 1861 (Federico 1991). We could, in principle, use these data to construct a ‘national’ trade series and, through them, gain insights into the real economy. In practice, it is a challenge. Differences in collection criteria, presentation, the efficiency of the statistical agencies, and the amount of smuggling between the pre-unification Italian states, make the construction of an aggregate series, at best, difficult. Only one state, Piedmont, has year-by-year trade data for the entire period 1815–61.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".