Late Colonialism and Postcolonial Development in Africa: A Comparative-Historical Analysis of Former Italian Africa
Bibliographic record
Abstract
This article analyzes the development legacies of Italian colonialism in Africa. The comparative-historical analysis shows that colonial Italy pursued “settler colonialism” in areas conducive to colonial settlement and large-scale exploitation, and “plantation colonialism” in areas with fewer resource endowments and settlement opportunities. In the immediate aftermath, while settler colonialism had a positive influence and plantation colonialism exerted a negative impact on economic prosperity, both types of Italian colonialism had strong negative effects on human development. In the post-1960 period, whereas the colonial legacy of plantation colonialism led to persistent poverty in Somalia, long-run development in Eritrea and Libya was contingent on critical junctures, which variously reinforced, destabilized, and/or transformed the institutional and developmental legacies of settler colonialism. I draw on the comparative-historical tradition emphasizing national orientation of European colonizers and natural conditions in colonized areas as key determinants of European colonialism and long-run development. However, I emphasize “factor endowments” as one such condition that defined Italian colonization strategies and institutions, finding little empirical support for factor endowments per se or precolonial ethnic centralization as principal determinants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".