Toward digitalization futures in smallholder farming systems in Sub-Sahara Africa: A social practice proposal
Bibliographic record
Abstract
This paper contributes to the digitalization of rural agriculture literature by proposing a social practice approach. Digitalization (practices) is conceived as an unfolding constellation of everyday farming activities manifested by practically conscious people meaningfully leveraging competences to integrate materials elements of life. Thirty-one expert key informants' interviews were conducted on experiences and pathways for the future of digital agriculture in Africa. Thematic analysis of the interviews revealed that materials (access to digital tools, enabling digital infrastructure, supporting social infrastructure), competencies (digital literacy among farmers and extension officers, IT and data education among populaces), and meanings (connecting digitization with local customs and norms and aligning digital tools with the values/perceptions of what farming is) are critical elements to establishing and embedding digital tools and services in everyday agriculture in Africa. Thus, I propose adopting a social practice approach (which focus on establishing and integrating materials, competencies, and meanings) to understanding, researching, and guiding processes of rural smallholder digitalization. The proposed approach, the first application of the social practice lens to smallholder digitalization, would allow for interventions that focus on establishing holistic and all-encompassing building blocks that bring digitalization practices to life. Specifically, the social practice proposal provides an outlook to move beyond the technologies –tools and services– of digitalization, to equally value the competencies required and meanings engendered in smallholder digital futures.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".