The Online Professional Master of Science in Food Safety Degree Program at Michigan State University: An Innovative Graduate Education in Food Safety
Bibliographic record
Abstract
A market-research study conducted in 2000 indicated a need for a degree program in food safety that would cover all aspects of the food system, from production to consumption. Despite this, such a program was not enthusiastically supported by employers, who feared losing their valued employees while they were enrolled in traditional on-campus graduate programs. A terminal professional degree was successfully created, offered, and modified over the succeeding five years. The innovative, non-traditional online program was developed to include a core curriculum and leadership training, with elective courses providing flexibility in specific areas of student interest or need. The resulting Professional Master of Science in Food Safety degree program provides a transdisciplinary approach for the protection of an increasingly complex food system and the improvement of public health. Enrollment in the program steadily increased in the first three years of delivery, with particular interest from industry and government employees. The curriculum provides a platform of subject material from which certificate programs, short-courses, seminars, workshops, and executive training programs may be delivered, not only to veterinarians but also to related food and health specialists. The program has fulfilled a need for adult learners to continue as working professionals in the workforce. The benefit to the employer and to society is an individual with enhanced knowledge and networking and leadership skills.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".