THE USE OF INTELLECTUAL PROPERTY RIGHTS AND INNOVATION BY MANUFACTURING FIRMS IN CANADA
Bibliographic record
Abstract
The objective of the paper is to determine how the utilisation of intellectual property rights (IPRs) by Canadian manufacturing firms is related to their characteristics, activities, competitive strategies and industry sector in which they operate. The principal source of information used in this endeavour is the Statistics Canada Survey of Innovation 1999. The paper starts with an overview of other studies that looked at the use of intellectual property rights in Canada. Follows a conceptual framework presenting variables likely to explain the use specific IPRs by Canadian manufacturing firms. The use of IPRs is to a great extent correlated with basic economic characteristics of firms, their activities and industry environment. A series of estimated logit regressions predict the probability that a firm will use a specific IPR instrument. Also estimated is the contribution of the use of IPRs to the probability that a firm innovates. The decision of a firm to use IPRs is often not independent of the decision to innovate. To eliminate the potential endogeneity bias I estimate a two-stage logit model. A comparison of the single- and two-stage logit models shows that the nexus from the protection of intellectual property (patents) to innovation may be weaker than indicated by the single equation model.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 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) | 0.003 | 0.000 |
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.
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".