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Record W1971137169 · doi:10.1080/11926422.2004.9673370

New paths to knowledge, innovation and development: Canadian leadership in emerging global partnerships

2004· article· en· W1971137169 on OpenAlexaffabout
Paul Dufour

Bibliographic record

VenueCanadian Foreign Policy Journal · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsKnowledge economyPolitical scienceCurrencyWork (physics)Private sectorBusinessOrder (exchange)Global LeadershipInvestment (military)Public relationsEconomic growthEngineeringEconomicsPoliticsFinance

Abstract

fetched live from OpenAlex

The establishment of various policy experiments such as the Canada Foundation for Innovation, the Canada Research Chairs, Genome Canada, and the Canadian Institutes of Health Research, not to mention increased funding for university research and the National Research Council, have succeeded in creating a knowledge renaissance both within the research community and through concomitant partnerships with the private sector, governments, and the higher education sectors. Knowledge is a universal currency, and innovation a global enterprise. Attention needs to be paid to building on Canada's legacy as a knowledge broker, and its future as a global leader in innovation, in order to maintain and improve its status as a leading economy for trade, investment, development and R&D. Discovery, skills and innovation know no boundaries in today's wired and inter‐connected world, and Canada is confronted with challenges from new and emerging knowledge producers. However, Canada has no strategic focus in place to meet these challenges or to tackle new opportunities in the global knowledge market. In short, Canadian decisionmakers and knowledge leaders have to think “intermestically”. Domestic and international decisions are two sides of the same coin.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.897
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.139
GPT teacher head0.330
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2004
Admission routes2
Has abstractyes

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