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Record W4377260469 · doi:10.1515/9780773572690

Rockefeller, Carnegie, and Canada

2005· book· en· W4377260469 on OpenAlexaboutno aff
Jeffrey Brison

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2005
Typebook
Languageen
FieldArts and Humanities
TopicTwentieth Century Scientific Developments
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

In the first half of the twentieth century, the Rockefeller Foundation and the Carnegie Corporation helped to create and maintain a cultural and intellectual infrastructure in Canada that benefited key institutions such as University of Toronto, McGill University, the National Gallery, the Humanities Research Council of Canada, and the Canadian Social Science Research Council. Jeffrey Brison documents how American philanthropy facilitated the transformation from a private, localized system of cultural, intellectual, and academic patronage to a complex, nation-based system of incorporated patronage - a system in which the major patron was the federal state. His study calls into question our essentialistic notions of contrasting national identities and the now-mythologized juxtaposition of an American culture fueled by the free market with a Canadian one sustained by state support.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0140.008
Scholarly communication0.0120.005
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0650.014

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 head0.013
GPT teacher head0.168
Teacher spread0.155 · 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.

Study designTheoretical or conceptual
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

Citations15
Published2005
Admission routes1
Has abstractyes

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