Investment in Intangible Assets in Canada: R&D, Innovation, Brand, and Mining, Oil and Gas Exploration Expenditures
Bibliographic record
Abstract
This paper presents estimates of intangible investment in Canada for the purpose of innovation, advertising and resource extraction. It first expands upon work by Beckstead and Gellatly (2003), Baldwin and Hanel (2003), Beckstead and Gellatly (2003), Beckstead and Vinodrai (2003) and Baldwin and Beckstead (2003) who argue that the scope of innovative activity extends beyond research and development (R&D) as defined by the Frascati Manual. It extends the definition of innovative activities to include all scientific and engineering expenditures - regardless of whether they are market-based or produced with a firm. The paper also considers expenditures on intangible items such as brands or resource exploration. The paper contributes to the existing literature by creating intangible investment estimates (science and engineering knowledge, advertising, mineral exploration by industry) using Statistics Canada's high quality and internally consistent databases. It produces estimates that accord with other intangibles studies (Corrado, Hulten and Sichel 2005, 2006; Jalava, Ahmavarra and Alanen 2007) and shows that traditional R&D type investment estimates account for about a quarter of intangible science and engineering investments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".