Bibliographic record
Abstract
for helping us with the European CIS 2 surveys. The first author also acknowledges financial support from SSHRC. This paper compares pairwise the innovation performance of Canada with France and Germany, respectively. The comparison is based on two ordered probit models with sample selection, one where innovation is measured by the introduction of new-to-the firm products and one where it is measured by the introduction of new-to-the market products. The econometric analysis attempts to explain part of the country differences as the result of the sectoral composition of output, and the effects of size, environment conditions (proximity to basic research and competition) and innovation activities (internal R&D, the number of innovation activities, cooperation and government support). The Canadian firms benefit from being larger and more numerous in receiving government support, but suffer from a lack of competition and internal R&D. These structural effects combined, while informative, are not enough to explain a lot of the basic pattern of innovation revealed by the raw data. If we take the stronger measure of firstto-market innovation as a yardstick of innovation, the observed pairwise country differences are less strong, and our model explains a little bit more of the observed differences.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.372 | 0.226 |
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".