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Record W2648046649 · doi:10.3390/jrfm14040151

Persistent Food Shortages in Venetian Crete: A First Hypothesis

2021· article· en· W2648046649 on OpenAlexvenueno aff
Irene Sotiropoulou

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicByzantine Studies and History
Canadian institutionsnot available
FundersTurun Yliopisto
KeywordsEconomicsColonialismCirculation (fluid dynamics)Economic shortageEmpireCurrencyValue (mathematics)EliteEconomyMonetary economicsPolitical sciencePoliticsLawGovernment (linguistics)

Abstract

fetched live from OpenAlex

This paper examines the persistent food shortages in the island of Crete under Venetian rule (1204–1669) through the prism of the monetary system of Venetian territories and in combination with the other economic policies of the Venetian empire. From the available sources and analysis, it seems that the policies of Venice which prioritised the food security of the metropolis, the financial support to the elites, and the elite-favouring monetary and taxation system were contradictory and self-defeating. In particular, the monetary structure of the colonial economy and the taxation system seem to have been forcing both Cretans and Venetian settlers to produce wine for export instead of grain despite the repeated food shortages. The parallel circulation of various high-value (white money) and low-value (black money) currencies in the same economy and the insistence of the Venetian administration to receive taxes in white money seems to have been consistently undermining the food security policy adopted by the same authorities. The paper contributes to the discussion of how parallel currencies can stabilise an economy or can create structural destabilisation propensities, depending on coeval economic structures that usually go unexamined when we examine monetary instruments.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.174
Teacher spread0.158 · 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 designNot applicable
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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