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Record W4402616188 · doi:10.1177/00207152241269033

Late Colonialism and Postcolonial Development in Africa: A Comparative-Historical Analysis of Former Italian Africa

2024· article· en· W4402616188 on OpenAlexvenueno aff
Salih Noor

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismPolitical scienceHistoryGeographyEthnologyAnthropologySociologyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.378
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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