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Record W2482149721 · doi:10.1017/cbo9781139524858.012

The Thirty Years War and the disruption of international finance, 1914–1944

2015· book-chapter· en· W2482149721 on OpenAlexaboutno aff
Larry Neal

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsStock exchangeEconomic historyStock marketSpanish Civil WarFinancial marketEconomyPolitical scienceBusinessEconomicsFinanceHistoryLaw

Abstract

fetched live from OpenAlex

The outbreak of the Great War in the summer of 1914 created a whirlpool of financial disturbances that disrupted completely the global financial market. Until then, international finance, operating both through banks with foreign branches and correspondents and securities markets open to corporations and customers both domestic and foreign, had been expanding worldwide. When Austria declared war on Serbia on Tuesday July 28, stock exchanges in Montreal, Toronto and Madrid closed, followed on Wednesday July 29, by the closure of exchanges in Vienna, Budapest, Brussels, Antwerp, Berlin, and Rome. On July 30, St. Petersburg and all South American countries closed, as did the Paris Bourse ; first on the Coulisse (the bankers’ market) and then on the Parquet (the official exchange). When even the London Stock Exchange shut down on Friday morning July 31, only the exchanges in New York remained as markets where the world's panic could vent. All this happened before the Great Powers themselves got around to declaring war. As with the outbreak of wars in the past, there was an immediate scramble for liquidity and the pound sterling rose sharply on the foreign exchanges (Keynes 1914). The shock of universal sell orders on all the world's stock exchanges was completely predictable, but two aspects were new and cause for future concern whenever the hostilities ended. First was the extent to which foreigners with open positions on the London Stock Exchange and with the London discount houses were unable to meet their obligations. The importance of the London money market for the finance of international trade meant that the outbreak of general hostilities inflicted what we now call “counterparty risk” upon the entire financial community of London. As the bulk of the world's international trade at the time was then financed through the London money market, whether a British firm was actually involved in the trade or not, counterparty risk reverberated throughout the world. The second problem encountered in London was the pusillanimity with which the London banking community met the systemic liquidity crisis (Keynes 1914, pp. 461–462).

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.005
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.026
GPT teacher head0.199
Teacher spread0.173 · 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
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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