Recalibrating Data on Farm Productivity: Why We Need Small Farms for Food Security
Bibliographic record
Abstract
In 2009, the ETC Group estimated that some 70% of the food that people globally consume originates in the ‘peasant food web’. This figure has been both embraced and critiqued, and more recent critiques have focussed on analysing farm productivity to offer some more precise estimates. Several analyses suggest that the proportion of small farms’ contributions to total food production is closer to one-third, arguing that the role of small food producers in food security are grossly exaggerated. We challenge this argument by re-tabulating the available farm productivity data to demonstrate that smaller farms continue to provide a significant proportion of food and are consistently more productive than their larger counterparts. We further posit that even our own interpretation falls short of estimating the full extent of small farms’ contributions, including non-monetary ones, like ecosystem services and community life, many of which run counter to the productivist model that drives large-scale industrial agriculture. We conclude that policies that support small farms are a global necessity for food security, as well as for transitions to more sustainable and more equitable food systems.
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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 | 0.020 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".