MétaCan
Menu
Back to cohort
Record W2083499681 · doi:10.1017/s0022050702001018

<i>Battles for the Standard: Bimetallism and the Spread of the Gold Standard in the Nineteenth Century</i>. By Ted Wilson. Aldershot: Ashgate, 2000. Pp. xi, 200.

2002· article· en· W2083499681 on OpenAlexaff
Angela Redish

Bibliographic record

VenueThe Journal of Economic History · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomicsEconomic historyCapitalismPoliticsNetwork effectExternalityGold standard (test)Gold as an investmentKeynesian economicsPolitical economyPolitical scienceMonetary economicsLaw

Abstract

fetched live from OpenAlex

In the early twenty-first century, the choice of monetary regime has again become a matter of political and economic debate. The European experiment in monetary union is being watched, with a mixture of hope and fear, by policymakers in the Americas, and most likely in Asia as well. Perhaps this explains the resurgence of interest in the battles over monetary regimes in the nineteenth century, when a world that had been more or less bimetallic for several centuries switched to the gold standard. Why did it happen? Why did it happen then? Was it a change for the better or not? What were the relative contributions of economic determinism, network externalities, and global capitalism? It is this “range of territory” (now I understand why this cliché is overused!) that Ted Wilson addresses, arguing—albeit in a multicausal framework—for the importance of network externalities, and suggesting that the gold standard occurred by default rather than design. Britain went on gold early and somewhat accidentally; Germany, France and the United States followed; aspiring borrowers on the periphery did not have much choice—if they wanted access to international capital markets—so they followed, too.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.561
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.202
Teacher spread0.180 · 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
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Economic HistorySame topicGlobal Financial Crisis and PoliciesFrench-language works237,207