Taking Youth Engagement to the Next Generation: Lessons from Best Youth Engagement Practices Toward Food Sustainability
Bibliographic record
Abstract
Food is one of life’s most basic necessities. Yet the problems of our food system are becoming increasingly worse: global food security is in jeopardy, health related diseases are epidemic, and generations are increasingly disconnected from their food. The youth population, in particular, is largely missing from the food engagement and decision--making process. Yet it is this group that will inherit the problems of the food system, and constitute the next generation of eaters, policy--makers, and planners. This paper aims to fill this gap by examining ways to improve youth engagement in food sustainability by making it more widespread, meaningful and effective. Using a scan and analysis of best practice research, this paper offers recommendations –including cases, tools, principles and techniques – for stakeholders (such as NGOs, local governments and municipal planners) to improve their youth engagement strategies in food sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".