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Record W2900613730 · doi:10.1111/hic3.12504

Recent trends in Middle East economic history: Cultural factors and structural change in the medieval period 650–1500 (Part one)

2018· article· en· W2900613730 on OpenAlexaff
Maya Shatzmiller

Bibliographic record

VenueHistory Compass · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsArgument (complex analysis)Middle EastIslamDysfunctional familyPeriod (music)Empirical evidenceHistoryMiddle AgesDevelopment economicsPositive economicsPolitical scienceAncient historyPsychologyEconomicsLawMedicineEpistemologyAestheticsPhilosophyArchaeologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Economic historians have returned in recent years to blaming the prolonged economic decline of the Middle East on cultural factors. At the root of the problem, as they see it, were economic institutions rendered inefficient by the religion of Islam and Islamic law, and they have used evidence from the Islamic medieval Middle East as support. This paper reviews the various strands of the argument and critically assesses its use of historical evidence. It concludes that the evidence does not support the postulate that cultural factors generated a dysfunctional Middle Eastern economic system and offers an alternative analysis. It shows that empirical evidence ignored by the cultural factors argument points to growth promoting structural change and corresponds to what we would expect based on economic theory.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.212
GPT teacher head0.297
Teacher spread0.085 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2018
Admission routes1
Has abstractyes

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