MétaCan
Menu
← Back to cohort
Record W2503182549 · doi:10.1057/9780230554689_6

Imperial Preference and the Anglo-American Loan Negotiations, September–December 1945

2002· book-chapter· en· W2503182549 on OpenAlexaff
Francine McKenzie

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2002
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNegotiationLoanGovernment (linguistics)DelegationPolitical scienceGenerosityEconomic historyFinanceEconomicsLaw

Abstract

fetched live from OpenAlex

The sudden end of the war brought relief and adversity to Britain. Long-anticipated economic difficulties mounted with alarming speed. The newly elected Labour government was desperate for aid to stave off a looming financial crisis. As Keynes had anticipated, the UK turned to the US for a loan. American officials were not surprised by the request, but they were not inclined to generosity. Washington believed there was now no longer a compelling reason to carry London. No matter what would result from Anglo-American financial negotiations, one thing was certain: US aid would come at a price. Securing British support for American postwar trade proposals was one obvious way of turning British weakness to American advantage. In particular, the US might be able to overcome British obstinacy over preferences by linking their commercial deliberations to the promise of financial assistance. Just as the Americans were intent on linking commercial and financial matters, British negotiators were determined to separate them. Consequently, the British delegation sent to Washington to negotiate the loan included no commercial experts in the hope that would limit the discussions to questions of finance. This ploy backfired. The Americans postponed financial talks until the British agreed to address outstanding commercial questions leading towards an outline for an International Trade Organization (ITO). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.030
GPT teacher head0.243
Teacher spread0.212 · 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
GenreOther

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 venuePalgrave Macmillan UK eBooks→Same topicPolitical and Economic history of UK and US→French-language works237,207→