Towards a theory of agrarian skilling (Or, why farmer knowledge does not stop at the edge of the field)
Bibliographic record
Abstract
Recent contributions to the literature on agricultural deskilling argue that the increasing commercialisation of smallholder agriculture and a reliance on externally developed technologies has undermined the environmental basis of farmer learning. Despite many compelling attributes, the initial contributions to the deskilling thesis insufficiently analyse key social dimensions of smallholder agriculture. Farming is not merely a technical activity and agricultural knowledge does not begin and end at the boundary of the fields. Rather, the pursuit of agriculture is a deeply social process and we must broaden our understanding of farmer knowledge to better incorporate the social dimensions of agriculture. Accounts of agricultural learning must therein address the skills through which farmers manage a range of relationships that underpin agricultural livelihoods, including complex market transactions, credit/debt relations, labour sourcing, off-farm employment and networks for accessing government schemes. This form of knowledge practice is what we call 'agrarian skilling' and stands as a necessary extension of the more bounded and technical notion of agricultural knowledge. Focusing on agrarian skilling in this manner allows greater analytical purchase on the power relations inherent to knowledge creation and dissemination within and across smallholder populations.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.039 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".