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Record W7039400661

Manifestations of Canadian nationalism and U.S.-Canadian relations (1963-1984)

2018· article· fr· W7039400661 on OpenAlexaboutno aff

Bibliographic record

Venuetheses.fr (ABES) · 2018
Typearticle
Languagefr
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsnot available
Fundersnot available
KeywordsNationalismPoliticsInternational relationsFace (sociological concept)
DOInot available

Abstract

fetched live from OpenAlex

Cette thèse étudie l'attitude de l'exécutif américain face aux manifestations de nationalisme canadiens ou s les gouvernements Libéraux de Lester Bowles Pearson (1963-­‐1968) et de Pierre Elliott Trudeau (1968-­‐79/1980-­‐84). Le nationalisme canadien multiforme est ici envisagé comme un objet sous le regard de l'exécutif américain ainsi qu'un facteur potentiel dans la relation États-Unis/Canada. Sous le gouvernement Pearson, le nationalisme canadien ainsi que la recherche de la prospérité poussent les gouvernements des deux côtés de la frontière à mieux coopérer. Face à des décisions de l'administration Nixon (1968-­‐74) et sous l'impulsion de pressions nationalistes intérieures, le gouvernement Trudeau opte pour des politiques qui visent à réduire la vulnérabilité de l'économie canadienne envers celle de son voisin. Ces mesures contribuent à légitimer l'intervention du gouvernement fédéral, renforcer son rôle et l'unité nationale canadienne. L'étude de la perception du nationalisme canadien par Washington montre non seulement une évolution du ton employé envers Ottawa mais également une maturation de la relation. Les politiques indépendantes du Canada poursuivies par les gouvernements Libéraux et la poursuite de l'intérêt national par Washington vont de pair avec une concertation accrue entre les deux voisins.

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)0.003
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: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.933

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0210.006
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.027
GPT teacher head0.256
Teacher spread0.229 · 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
Published2018
Admission routes1
Has abstractyes

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