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Record W3201127614 · doi:10.1093/scipol/scab057

Canada’s changing innovation landscape

2021· article· en· W3201127614 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueScience and Public Policy · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDiversification (marketing strategy)TrademarkIntellectual propertyBusinessInternational tradeMarketingPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Our objective is to study Canada’s patenting activity over time in aggregate terms by destination country, by assignee and destination country, and by diversification by country of destination. We collect bibliographic patent data from the Canadian Intellectual Property Office and the United States Patent and Trademark Office. We identify 19,957 matched Canada–US patents, 34,032 Canada-only patents, and 43,656 US-only patents from 1980 to 2014. Telecommunications dominates in terms of International Patent Classification technologies for US-only and Canada–US patents. At the firm level, the greatest number of matched Canada–US patents were granted in the field of telecommunications, at the university level in pharmaceuticals, at the government level in control and instrumentation technology, and at the individual level in civil engineering. We use entropy to quantify technological diversification and find that diversification indices decline over time for Canada and the USA; however, all US indices decline at a faster rate.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.086
GPT teacher head0.239
Teacher spread0.153 · 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