Impediments to the Ability of Canadian Food‐Processing Firms to Compete: Evidence From a Survey on Innovation
Bibliographic record
Abstract
ABSTRACT Factors that impede Canadian food‐processing firms’ ability to compete in domestic and international markets are explored. Business micro‐data were obtained from the Survey on Innovation in the Food Processing Industry in 2004 ( n = 809). A higher Canadian dollar against the U.S. dollar was judged to be the most severe of the 10 impediments to the ability of Canadian food processors to compete. Two broader categories of the 10 impediments emerged from a principal component analysis. One category of the impediments relates to private and public standards, while the other relates to vertical and horizontal market power issues. The severity of these impediments is not significantly different among the different size classes of food processors. Subsequent clustering procedures based on these impediments found that the food processors in one cluster were more constrained by all these impediments than their counterparts in the other cluster. Establishment characteristics, such as food subsector classification, and firm strategies, such as export orientation and engagement in R&D activities, were systematically associated with a firm's likelihood of being in the cluster that perceived the impediments to be more severe. Exporting firms are more likely than other firms to perceive each one of the impediments as a constraint. [EconLit Subject Matter Areas: D220; L200; L660].
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".