Bibliographic record
Abstract
3.1 Between roughly 1860 and 1940 Italy went from being a poor, backward, primarily agricultural country to a relatively prosperous, modern industrial economy. There were still pockets of poverty and backwardness, especially in the South, and agriculture was still the dominant economic activity, but the country also possessed a large, modern and competitive industrial sector. By 1951, the date of the first post-WWII census, agriculture employed less than 50 per cent of the active population for the first time in the country's ninety-year history (cf. table 2.3). The purpose of the macroeconomic models of growth and fluctuations is, in general, to help us understand the nature and timing of this transformation. Two views of Italian economic development dominate the literature. In one, it is seen as a success, even though modest; in the other, as a failure, even though partial – the two positions are the scholarly equivalent of the optimist's half-full and the pessimist's half-empty glass of water. Adherents to the former view once again fall into two groups. Those in the first argue that in the 1880s (Romeo 1961, 1963) or 1890s (Gerschenkron 1968) Italy underwent a discontinuous jump in industrial production that marked the beginning of its modern economic growth. Their objective is to pinpoint the spurt and to explain why it occurred when and where it did. Those in the second maintain, instead, that the observed surge in the growth rate was merely a positive cyclical fluctuation around a rising trend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".