Conclusion: Youth Aspirations, Trajectories, and Farming Futures
Bibliographic record
Abstract
Abstract This book commenced with a question of global importance: in a world in which farming populations are ageing, who is going to provide the planet’s peoples with the “sufficient, safe and nutritious food” that is needed to meet the “dietary needs and food preferences for an active and healthy life” (FAO 2006)? In other words, where are the people who are needed to generationally renew farming? As explained in the introduction, addressing this question meant going against the grain of much research on youth and agriculture. Rather than seeking to understand youth’s apparent disinterest in farming and their exodus from the countryside, the research teams focused on those youth and young adults who stayed in, returned, or relocated to rural areas and were involved in farming (often alongside various other economic activities). Thereby, the case studies presented in this book have put in the spotlight the next generation of farmers. In this concluding chapter, we draw out some important issues emerging from across the chapters and reflect on key differences. This way, we reiterate the various pathways of becoming a farmer, the main challenges experienced by these young farming women and men, and the roles that policies and organizations could play in facilitating the process of becoming a farmer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".