A model for education and promoting food science and technology among high school students and the public
Bibliographic record
Abstract
A model for education and promoting food science and technology (FST) as a career among high school students and the public is proposed. Important as FST may be, there has been a general down trend in the number of students enrolling for the course in the institutions worldwide. This is not unconnected with the “home economics/catering” image perception of the discipline by the public. The efforts of some developed countries in reversing this trend were reviewed. The USA, UK, Australia and Canada have put activities in place to this end, hence their stride in food security. If developing continents like Africa will overcome food insecurity, deliberate effort should be geared in making sure FST as a discipline/profession, receives the proper image and boost in enrolment. The proposed model uses the food chain to make a distinction between FST and other food-related professions such as home economics, hospitality management and nutrition/dietetics. FST operates at the secondary stage (processing and distribution) of the food chain closer to the farm gate, providing its end product (food) for other professions while targeting the public. All the other food related disciplines operate at the tertiary stage (retail) directly with the consumer while depending on the product of FST. The core business of the food industry is the product, process and the company, with FST directly involved in all of these areas. The model also highlights the involvement of FST in these areas as well as the need for industry-academia partnership. Key words : Food science and technology, image, home economics, dietetics, nutrition, food chain.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".