Bibliographic record
Abstract
7.1 Economic growth, more than any other feature, denned this period and shaped the literature. The reasons for this are easy to understand. Rates of growth of GDP, GDP per capita, total factor productivity, and output per worker were higher and more sustained than at any time in the country's history. It was as if, almost a century after unification, domestic and international economic conditions conspired to release the country's considerable economic potential. The growth, moreover, was associated with dramatic structural change. In 1950, agriculture was still the dominant sector, accounting for over 40 per cent of total employment and 25 per cent of value added. By the early 1960s, agriculture no longer dominated the Italian economy – in terms of both employment and value added, industry and services were larger. As a result, Italy finally joined the league of industrial nations - and never looked back. Agriculture during this period was itself transformed. Output grew rapidly but, of greater importance, the growth of labour productivity was even larger so that the sector released labour even as its output expanded. As Petri (1997a, p. 368) and others observe, inflation remained modest, certainly by Italian standards, throughout the period of rapid growth. This was the result of a conscious attempt by the Bank of Italy to moderate increases in the money supply and by the government to rein in budget deficits. Rates of investment and saving were the highest in the history of modern Italy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".