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Record W4308141403 · doi:10.1134/s1019331622050112

Science Policy in Canada

2022· article· en· W4308141403 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHerald of the Russian Academy of Sciences · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Political scienceStrengths and weaknessesPopulationState (computer science)Science policyPublic administrationSociologyDemographyPsychology

Abstract

fetched live from OpenAlex

The formation and current state of science policy in Canada are analyzed. Attention to this topic is explained by the fact that the country is a member of the G7 of the leading industrialized countries, although its population is only 0.5% and its GDP is about 2% of the global numbers. By international standards, Canada is not a leader in scientific and technological advance, its specificity being that, with relatively low R&D spending, it occupies leading positions in terms of indicators such as the number of scientific publications in international databases and the number of Nobel laureates (in the last 13 years alone, seven Canadian scientists have become Nobel prizewinners). Canadian affiliation makes up 3.6% of articles published in peer-reviewed journals worldwide. The evolution of the mechanisms of government support for science in Canada is traced, and current practices are summarized. The strengths and weaknesses of the Canadian model of organization of science are identified. This experience may be of interest to Russia.

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.

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)0.016
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Science and technology studies, Open science
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.021
Science and technology studies0.0010.005
Scholarly communication0.0000.001
Open science0.0110.002
Research integrity0.0000.001
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 head0.107
GPT teacher head0.415
Teacher spread0.308 · 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