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Record W4380142852 · doi:10.5539/res.v15n2p1

Interorganizational Relationships in Medieval Trade: An Analysis of the Hanseatic League

2023· article· en· W4380142852 on OpenAlexvenueno aff
Eric G. Kirb

Bibliographic record

VenueReview of European Studies · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueDominance (genetics)FeudalismEconomyPolitical scienceEconomicsLawPolitics

Abstract

fetched live from OpenAlex

The Hanseatic League was a commercial federation of guilds and cities in the Baltic region that dominated trade in northern Europe during the later Middle Ages. At its peak, it linked traders and market towns from England to Russia and most ports in between.  It worked to remove trade barriers and provide security to its members. Employing an analytically structured approach, this study analyzes secondary sources to investigate the relationships between the members of the Hanse as well as the primary motivations driving the formation of the Hanseatic League. When this is analyzed as a federation style of interorganizational relationship, the five defining key contingencies become apparent: (1) power asymmetries in the High Middle Ages existed for the merchants with the balance of power in favor of monarchs; (2) individual guilds found it beneficial to establish ongoing relationships with other guilds; (3) economies of scope and scale allowed for efficiencies that would lead to trade dominance; (4) merchants sought more stable and predictable open access to markets across northern Europe; and (5) with the decline of feudalism, guilds sought to increase the acceptance and privilege of their community.  The Hanseatic League’s formation was also based on a sixth key factor: security for its members.

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: none
Teacher disagreement score0.529
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.150
GPT teacher head0.295
Teacher spread0.145 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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