Investment in research and development and export performances of Canadian small and medium‐sized agri‐food firms
Bibliographic record
Abstract
Abstract The objective of this study is to analyze the impact of research and development (R&D) investment on the export performance of Canadian agrifood small and medium‐sized enterprises (SMEs) and on that of related sectors, namely, the textile and clothing sector and the manufacture of leather goods and similar products. First, we analyzed explanatory factors for R&D expenses, and then, we analyzed the impact of R&D on extensive (market access) and intensive (trade value) margins of trade using a difference‐in‐differences approach. We used data obtained from the Statistics Canada's National Accounts Longitudinal Microdata File (NALMF) for 2010–2015 and the Trade by Exporter Characteristics (TEC) database. The size of firms and their support from the Canadian government affect their propensity to invest in R&D and the amount of R&D expenses and their intensity, measured as the ratio of R&D to sales of goods and services. Overall, our results show that investment in R&D has a positive impact on the export performance of agrifood SMEs; the impact is smaller when the destination is one of the states in the United States.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".