Adaptation and development pathways for different types of farmers
Bibliographic record
Abstract
One of the greatest challenges humanity faces is feeding the world's human population in a sustainable, nutritious, equitable and ethical way under a changing climate. Urgent transformations are needed that allow farmers to adapt and develop while also being climate resilient and contributing minimal emissions. This paper identifies several illustrative adaptation and development pathways, recognising the variety of starting points of different types of farmers and the ways their activities intersect with global trends, such as population growth, climate change, rapid urbanisation dietary changes, competing land uses and the emergence of new technologies. The feasibility of some pathways depends on factors such as farm size and land consolidation. For other pathways, particular infrastructure, technology, access to credit and market access or collective action are required. The most viable pathway for some farmers may be to exit agriculture altogether, which itself requires careful management and planning. While technology offers hope and opportunity, as a disruptor, it also risks maladaptations and can create tradeoffs and exacerbate inequalities, especially in the context of an uncertain future. For both the Sustainable Development Goals and the 2015 Paris Agreement to be achieved, a mix of levers that combine policy, technology, education and awareness-raising, dietary shifts and financial/economic mechanisms is required, attending to multiple time dimensions, to assist farmers along different pathways. Vulnerable groups such as women and the youth must not be left behind. Overall, strong good governance is needed at multiple levels, combining top-down and bottom-up processes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".