The socio-economic determinants of social capital and the mediating effect of history: <i>Making Democracy Work</i> revisited
Bibliographic record
Abstract
Putnam argued that the different levels of social capital between the North and the South of Italy originated in the Middle Ages. In the North of Italy, the existence of a dense network of medieval towns gave rise to horizontal ties and collective action. Conversely, in the South of Italy, the authoritarian Norman rule generated hierarchical relationships and the absence of collective action. This article proposes an alternative explanation for the lack of social capital in the South of Italy using a comparative perspective. The analysis is undertaken in two steps: 1) testing the socio-economic determinants of social capital in 85 European regions; 2) performing a comparative historical analysis between deviant – that is, South of Italy and Wallonia – and regular – that is, North East of Italy and Flanders – cases. These cases are selected by looking at the residual of the regression model. The results suggest that medieval history does not explain the lack of social capital in the South of Italy. On the contrary, the historical legacy mitigates the negative effect of inequitable income distribution, low labour market participation and weak national cohesion on social capital.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".