Market-Related Capabilities of Ontario Meat Processing Firms in a Regulated Environment
Bibliographic record
Abstract
Historically, competitiveness in agri-food industries in Canada has been largely discussed using traditional economics-based approaches looking at market-level issues. Similarly, reports critical of supply management regulations in the Canadian meat processing industry have been largely based on opinion or ad hoc case studies and on market-level impacts that result from production quotas and price controls. Because these reports focus on issues at the market-level, the way that firms conduct business in supply-managed environments is not fully understood. The objective of this research was to apply several management approaches to understanding firm-level activities and to describe capabilities firms use to compete in three specific regulatory environments: firms using no supply-managed inputs, firms using only supply-managed inputs; and, firms using inputs from both regulatory environments. The meat processing industry in Ontario was chosen for study because firms are found in all three regulatory environments. This qualitative research used a multiple case design (Yin, 2009) and collected data from key industry contacts, from intensive interviews with managers of meat processing firms in three regulatory contexts, and from data from firm websites. The evidence collected suggests that the way managers view threats and opportunities in the external environment varies perhaps due to the way that managers think about the work of the firm, the way they approach competition, or their assessment of general and specific regulatory environments. All managers, however, described a lack of predictability in the industry environment and scarce time resources as key concerns. Evidence suggests that the general regulatory environment was a greater concern for managers than was the specific supply-managed environment. In addition, supply management regulations may create value for some meat processors by increasing predictability and saving time resources. The results describe ten market-related capabilities used by meat processing firms in the general regulatory environment; purchasing, industry knowledge and time-related capabilities may differ for firms in specific, supply-managed regulatory environments. A new framework is developed to extend our understanding of how firms in specific regulatory environments may use market-related capabilities to compete. Recommendations are discussed for firm managers, policy makers, and marketing boards.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".