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Record W2615775852 · doi:10.5539/res.v9n2p296

A Study of the Causes of Famine in Iran during World War I

2017· article· en· W2615775852 on OpenAlexvenueno aff
Mohammad Reza Pordeli, Malihe Abavysany, Maryam Mollashahi, Doost Ali Sanchooli

Bibliographic record

VenueReview of European Studies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFamineIndependence (probability theory)NeutralityWorld War IIPoliticsEconomic shortageDevelopment economicsAgriculturePopulationPolitical scienceSpanish Civil WarEconomic historyEconomyPolitical economyEconomicsGeographyLawSociologyDemographyGovernment (linguistics)

Abstract

fetched live from OpenAlex

In the early twentieth century, for various political and economic reasons, the European countries were divided into the Allies and the Central Powers which led to the beginning of World War I. During those years, Iran was politically and economically too weak. Despite the fact that Iran declared neutrality in this war, it attracted the attention of world powers because of its vast oil resources and especial geographical location. In this way, Iran too was affected by the war. At the time Iran had lost its political independence due to certain colonial contracts (e.g., 1907, 1915). With the start of the war, a large group of foreign troops occupied Iran. This in fact was a heavy blow to the economics and agriculture of Iran, and together with the successive droughts, marked the most extreme famine of the century in Iran. Food shortage, high prices, disease contagions, and the pressure of the foreign forces to collect food supplies, increased the mortality rate so much so that almost half of the Iranian population died in dire conditions.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.337
GPT teacher head0.524
Teacher spread0.187 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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