Embracing the Data Revolution for Development: A Data Justice Framework for Farm Data in the Context of African Indigenous Farmers
Bibliographic record
Abstract
This article examines the challenges that the digitalisation of agriculture in Africa brings with respect to ownership and control of data from the perspective of African indigenous farmers as data originators. It discusses the phenomena of the data revolution and digital agriculture in Africa, mapping out the ecosystem of digital agriculture by identifying general trends, key players, types and features of digitalisation driven by the capabilities of mobile and network infrastructure as well as by higher-level digitisation supported by data infrastructures capability. By situating farm data as a constitutive element of traditional knowledge of agricultural production that is subjected to datafication, the article outlines the challenges of access to data and of unequal utilisation of data as having an impact on development imperatives that necessitate better control of data flows. It proposes data justice as a conceptual framework for an Africa-wide governance of farm data in which the challenges on access to data and unfairness in its utilisation are addressed in a manner consistent with the continent’s aspirations for intra-regional relations
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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 | 0.044 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.111 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".