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Record W3161001785 · doi:10.1111/sjpe.12277

Structural change and economic development in the Islamic Middle East 700–1500: Population levels and property rights

2021· article· en· W3161001785 on OpenAlexaff
Maya Shatzmiller

Bibliographic record

VenueScottish Journal of Political Economy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsIslamUnderdevelopmentProperty rightsEconomicsPopulation growthPopulationProperty (philosophy)ShariaTechnical changeRent-seekingFertilityDevelopment economicsEconomic systemEconomic growthPolitical scienceLawSociologyGeographyMicroeconomics

Abstract

fetched live from OpenAlex

Abstract Economic historians have recently treated Islamic institutions as “growth retarding,” “averse to change,” “path dependent,” “lacking creative destruction” and “extractive” rather than “inclusive” and blamed their failure to change for the underdevelopment of the Middle East. This paper argues for a different approach to Islamic institutions which implies that they boosted economic growth. The paper explores changes in economic structures, a transition in population levels from high to low and a transition to individual property rights and provides economic indicators of growth. Articulated via Islamic law, legally sanctioned birth control and women's property rights were instrumental in assuring low fertility rates and equitable and well‐distributed income in the population. The paper concludes theoretically and empirically that Islamic institutions were innovative, amenable to change and efficient.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.095
GPT teacher head0.289
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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