Innovation quality and manufacturing firms' performance in Canada
Bibliographic record
Abstract
The overall objective of this paper was to determine the impact of producing a world-first innovation, a Canada-first innovation and a first-to-the firm innovation on firms' economic performance (employment, labour productivity, market share and total value added). The study used unique data from Statistics Canada's Citation1999 Survey of Innovation that was linked to the 1997 Annual Survey of Manufactures. Three hypotheses were tested: that innovative firms (firm-first, Canada-first, world-first) should have higher performance (in terms of the performance measures that are defined in the next section) than non-innovative firms; that the dichotomous innovation variables should be statistically different from zero in the multivariate analysis; that the estimated coefficients in the performance regressions should be greater for world-first innovations compared to firm-first innovations. In the regressions world-first innovators had higher employment and market share offering support for the first hypothesis, while the results for labour productivity and total value added were not statistically significant. With regard to hypothesis two, the multivariate results were somewhat mixed since the world-first innovator was significant in two performance equations. Hypothesis three was confirmed since in all cases the ordering on coefficient size for the performance variables was world, Canada, and firm (with world being the largest and firm being the smallest).
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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 | 0.002 | 0.011 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".