Bibliographic record
Abstract
Abstract The world’s crisis-ridden agriculture and food systems, besides huge environmental challenges, are facing a looming problem of generational renewal. Farming populations are ageing, many farmers appear to have no successor, and it is widely claimed that young people are not interested in farming; smallholder farming in its present state appears to be so unattractive to young people that they are turning away from agricultural futures. Will there be a new generation of farmers to take the place of today’s ageing farmers? What are the experiences of young people who are establishing themselves as farmers, and how are these pathways gendered? How can young farmers be supported to feed the world’s growing population? These are the questions that stimulated us and our colleagues in Canada, China, India, and Indonesia to join together in the multi-country research project, Becoming a Young Farmer: Young People’s Pathways into Farming in Four Countries. Each team used multi-sited case study research to bring to life the experiences of young farmers and would-be farmers, the various challenges they face, and important differences in their experiences both within and between the countries and study sites. By concentrating on women and men who have managed, or are trying, to set up their own farming livelihoods at a relatively early stage in their lives, we aimed to contribute both to theory by clarifying the generational dimension in the social reproduction of agrarian communities, and to policy by clarifying the barriers that young rural men and women confront in accessing land and other resources as well as the role of policies, institutions, and young people’s own individual and collective efforts in overcoming these barriers.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".