MétaCan
Menu
Back to cohort
Record W3123880190

International Trade and Institutional Change: Medieval Venice's Response to Globalization

2012· article· en· W3123880190 on OpenAlexaff
Diego Puga, Daniel Trefler

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDynamismContext (archaeology)De factoParliamentGlobalizationCompetition (biology)Capital (architecture)Political sciencePoliticsInternational tradeEconomicsEconomyGeographyMarket economyLaw
DOInot available

Abstract

fetched live from OpenAlex

Abstract: International trade can have profound effects on domestic institutions. We examine this proposition in the context of medieval Venice circa 800–1350. We show that (initially exogenous) increases in long-distance trade enriched a large group of merchants and these merchants used their new-found muscle to push for constraints on the executive i.e., for the end of a de facto hereditary Doge in 1032 and for the establishment of a parliament or Great Council in 1172. The merchants also pushed for remarkably modern innovations in contracting institutions (such as the colleganza) that facilitated large-scale mobilization of capital for risky long-distance trade. Over time, a group of extraordinarily rich merchants emerged and in the almost four decades following 1297 they used their resources to block political and economic competition. In particular, they made parliamentary participation hereditary and erected barriers to participation in the most lucrative aspects of long-distance trade. We document this ‘oligarchization ’ using a unique database on the names of 8,103 parliamentarians and their families ’ use of the colleganza. In short, long-distance trade first encouraged and then discouraged institutional dynamism and these changes operated via the impacts of trade on the distribution of wealth and power.

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)0.003
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 score0.862
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.312
Teacher spread0.284 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicCulture, Economy, and Development StudiesFrench-language works237,207