A Good Practice Project Related To Sustainable Solutions to Addressthe Global Pandemic and Achieve Agenda 2030
Bibliographic record
Abstract
The COVID-19 pandemic has stressed the relevance of performing a deep review regarding the robustness of current food production and consumption systems. The health crisis derived from the outbreak has directly influenced lifestyle habits throughout the planet, including food consumption and its related food loss and waste (FLW) generation, mainly by the compulsory "staying home". The reduction of FLW is a key to achieving sustainability, and more recently a main objective of the EU Farm to Fork (F2F) strategy for sustainable food, which aims at making food systems fair, healthy and environmentally-friendly. In this context, EC will strengthen educational messages on the importance of reducing food waste within school. Italian Project: "Food waste, consumer attitudes and behaviour: a project exploring the reasons linked to consumer-related food waste, involving Italian schools" (named SPAIC), was carried out by Ministry of Health, INAIL and three Italian high schools (from 2016 to 2020). SPAIC project was chosen by Food and Agriculture Organization of the United Nations (FAO) as good practice and lesson on food security and nutrition policy implementation in Europe and Central Asia region. Reducing the amount of wasted food is a key element in developing programs of global environmental, ethical and sustainable food system production. Food waste occurs at all stages of the food production, starting from harvesting, through manufacturing and distributing and finally consumption, but the largest contribution to food waste occurs surprisingly at home in the developed countries. To reduce consumer-related food waste, it is necessary to have a clear understanding of the factors influencing food waste-related consumer perceptions and behaviours. The Project focused on the food consumption to explore the reasons of food waste production at family level in order to overcome food-wasting behaviour. Then, the aim was to point out options to design prevention measures by the responsible involvement of the students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".