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Record W2495272874

Lessons from the Legacy of Canada-China

2013· article· en· W2495272874 on OpenAlexaboutno aff
Ruth Hayhoe, Julia Pan, Qiang Zha

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsChinaPolitical scienceContext (archaeology)Agency (philosophy)Section (typography)PoliticsHigher educationPublic administrationEconomic growthSpecial sectionPublic relationsSustainable developmentRegional scienceLibrary scienceSociologyEngineeringSocial scienceGeographyLawBusinessEconomics
DOInot available

Abstract

fetched live from OpenAlex

This article looks at a series of university linkages between Canadian and Chinese universities that were supported by the Canadian International Development Agency as a result of a development agreement signed in 1983 between the two governments. It first reviews relevant theoretical literature on higher education in a global context, and discusses the methodology adopted for the study. Then it provides an overview of a major program of collaboration in management education between 1983 and 1996, presenting views of leaders and participants on both sides. The next section overviews parallel linkages in the areas of education, engineering, agriculture, and medicine over the period from 1988 to 2001, and draws on the literature around university partnerships to identify factors that led, in some cases, to long-term sustainable relationships, but not in all. The final section of the paper reviews two major culminating linkages in environment and law, and suggests that these may have significant lessons for current and future cooperation between Chinese and Canadian universities in a new era of global geo-politics.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.136
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0250.013
Scholarly communication0.0100.003
Open science0.0010.004
Research integrity0.0030.004
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 head0.015
GPT teacher head0.289
Teacher spread0.274 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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