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Verbal Aggression Between Allies: Canada in Donald Trump’s Trade War Rhetoric

2021· article· en· W4200482053 on OpenAlexaboutno aff
Tomasz Soroka

Bibliographic record

VenuePoliteja · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRhetoricProtectionismSympathyPolitical scienceIdeologyPolitical economyLawMedia studiesSociologyPoliticsEconomicsInternational tradePsychologySocial psychology

Abstract

fetched live from OpenAlex

The article explores Donald Trump’s protectionist rhetoric relating to bilateral trade relations between Canada and the U.S. In particular, it presents how Trump’s isolationist economic platform evolved into trade war rhetoric and how this rhetoric affected Canada. To that end, the article analyzes President Trump’s statements and policies regarding the renegotiations of NAFTA, his administration’s tariff policies relating to imports of Canadian softwood lumber, steel and aluminum, and Trump’s opinions published in social media, mainly on Twitter. It also takes a comparative look on Donald Trump’s and Justin Trudeau’s ideological profiles to explain Trump’s lack of sympathy and hardline rhetoric against Canada.

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.112
Threshold uncertainty score0.815

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0450.032
Scholarly communication0.0150.005
Open science0.0020.006
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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 designQualitative
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

Citations6
Published2021
Admission routes1
Has abstractyes

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