Understanding the implementation of enhanced food safety controls in the Ontario food processing sector
Bibliographic record
Abstract
The challenge of ensuring food safety has become more important with an increased awareness of the level and nature of food-borne illness and associated economic and social costs. This has motivated governments and the food industry to implement food safety controls such as GMPs and HACCP to strengthen consumer confidence and manage risk. Firms assess the costs and benefits of implementing food safety controls in the context of the regulatory environment and marketplace demands. At the same time, although there may be economic incentives to enhance food safety controls, barriers may prevent firms from actually implementing such systems. This study investigates the process through which controls are implemented in the Ontario food processing sector and quantifies the motives for and barriers to adoption of food safety controls. The 'HACCP Advantage ' program developed by OMAFRA was used as the case study. Data collection involved a postal survey of food processing plants in Ontario of different firm sizes and in different sub-sectors. A total of 4,001 plants were mailed a questionnaire in October 2005, and 714 responses were returned, of which 363 were eligible. The results indicate that the main motivator for implementing HACCP is associated with 'commercial benefits' in terms of explaining the variability across the sample. Other factors such as 'enhanced business performance', 'good practice', and 'external pressure' were also considered important motivators to HACCP adoption. A predominant barrier to the implementation of HACCP was associated with 'business/market uncertainty'. Other barriers associated with 'organizational failure', 'financial limitations', and 'lack of knowledge' were considered important. The results suggest a number of policy recommendations and strategies through which the motives for adopting enhanced food safety controls may be strengthened and/or barriers reduced. Also, ways in which non-federally registered food processors, especially SMEs, may gain commercial and/or market advantages from adopting enhanced food safety controls are explored.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".