Public and private food safety regulatory approaches: exploring the role of global food safety initiative auditors in achieving public health objectives, focusing on the Canadian experience
Bibliographic record
Abstract
Governments are faced with a variety of challenging issues that have proven difficult to manage, one of which is providing safe food to its citizens. Recognizing this, and in response to several high-profile food safety crises in the late 1990s, food retailers created the Global Food Safety Initiative (GFSI), a private food safety regulatory approach. Certification to a GFSI-benchmarked private standard is often required through customer-supplier contracts, and as a result, food producers in the global agri-food supply chain may be subject to both the public and a private food safety regulatory approach. This dissertation uses Webb’s (2005) sustainable governance framework, which maintains that public, private, and civil sectors’ institutions, processes, rule instruments and actors have regulatory capabilities in support of public policy objectives, to explore whether or not the GFSI auditor, an actor in the GFSI-system, supports the public heath objectives of the state. Three primary research questions were developed to pursue this inquiry. First, on a functional level, can the GFSI auditor can be considered a public health practitioner analogous to the government’s food safety inspector? Second, do GFSI auditors view themselves as public health practitioners? Third, do other actors in the GFSI-system consider GFSI auditors to be public health practitioners? Using a mixed methods investigative approach, this dissertation presents the following conclusion: though the GFSI auditor can be characterized as a public health practitioner who supports the state’s public health objectives, neither the auditors themselves nor other actors, e.g. representatives of Certification Bodies, Certification Programme Owners, etc., in the GFSIsystem who participated in this research characterize the GFSI auditor as a public health practitioner. The final chapter of this dissertation discusses the public health and policy study significance of this investigation, provides policy recommendations to both the public and private institutions and actors involved governing food safety in Canada intended to strengthen the overall public health system by recognizing the role that GFSI auditors have in promoting public health objectives, and opportunities for further research.
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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 | 0.040 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.040 | 0.047 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| 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".