Do Smallholder, Mixed Crop-Livestock Livelihoods Encourage Sustainable Agricultural Practices? A Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As calls for bolstering ecosystem services from croplands have grown more insistent during the past two decades, the search for ways to foster these agriculture-sustaining services has become more urgent. In this context we examine by means of a meta-analysis the argument, proposed by Robert McC. Netting, that small-scale, mixed crop-livestock farming, a common livelihood among poor rural peoples, leads to environmentally sustainable agricultural practices. As predicted, mixed crop-livestock farms exhibit more sustainable practices, but, contrary to predictions, a small scale of operation does not predict sustainability. Many smallholders on mixed crop-livestock farms use sustainable practices, but other smallholders practice a degrading, input-scarce agriculture. Some large farm operators use soil-conserving, minimum-tillage techniques while other large operators ignore soil-conserving techniques and practice an industrialized, high chemical input agriculture. The strength and pervasiveness of the link in the data between mixed crop-livestock farming and sustainable agricultural practices argues for agricultural policies that promote mixed crop-livestock livelihoods.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it