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Record W2519480291 · doi:10.1177/0020702016666014

Revisiting the Canadian–Soviet barter proposal of 1932–1933: The Soviet perspective

2016· article· en· W2519480291 on OpenAlexaffabout
Kirk Niergarth, J. L. Black

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsCarleton UniversityMount Royal University
Fundersnot available
KeywordsBarterDistrustGovernment (linguistics)IdeologyPolitical sciencePolitical economyEconomyEconomic historyEconomicsLawPoliticsMarket economy

Abstract

fetched live from OpenAlex

In the autumn of 1932, when Canada had massive agricultural surpluses and hunger was afflicting millions of Soviet citizens, a proposal to trade Canadian cattle for Soviet fuels attracted considerable public support. Negotiated by a syndicate of Canadian businessmen, the cattle–oil barter deal was initially stalled because Conservative prime minister R.B. Bennett was ideologically opposed to it. Soviet documents suggest that this was not the end of the story. Revisiting the cattle–oil barter in light of these documents complicates current accounts of Canadian–Soviet relations in this period and raises questions about the Bennett government’s attitude to international trade and sensitivity to the kind of public pressure exerted by the proposal’s many Canadian supporters. In spite of major obstacles, the potential mutual benefit of the project nearly overcame ingrained mutual distrust.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.793
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0430.028
Scholarly communication0.0120.003
Open science0.0020.004
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0070.000

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 head0.008
GPT teacher head0.290
Teacher spread0.282 · 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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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