Factors impacting innovative activity in western Canadian food processing firms
Bibliographic record
Abstract
The industrial restructuring and technological change in the agriculture industry has limited employment opportunities and income in some rural areas. Food processing is one of the ways proposed to add value to agricultural products and provide employment opportunities and economic growth in rural areas. Worldwide, the food processing has seen growth stagnate, and the Canadian food processing industry is no exception. For long term growth, food processing firms must adopt innovation.The development and implementation of innovation by food processing firms is influenced by six main factors. Access to product markets, labour availability and the network of a firm are some of the factors that influence innovation activity. The attributes of a firm, the competitive conditions a firm faces and the characteristics of the region where the firm locates also influence the innovation decisions of food processing firms. The innovation survey developed by the Canadian Agricultural Innovation Research Network, and distributed to 1,200 food processors in Western Canada links these factors and innovation activity.Access to a large population and household amenities, such as skilled labour and business services, increases the probability that food processors in Western Canada will participate in innovation activities. Newer, larger firms and firms that could access knowledge spillover from other firms and industries also had a greater probability of introducing innovation. Therefore food processing firms within 400 km of an urban center are more likely to participate in innovative activities then food processing firms in remote rural areas.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".