An analysis of the policies of Information and Communication Technologies for Agriculture in Mali
Bibliographic record
Abstract
To harness the potential of Information and Communication Technologies (ICTs), developing countries need to develop national ICT policies that will serve as a framework for integrating ICTs at all levels of society. In the absence of that, different actors often engage in various actions for the same beneficiaries and in pursuit of the same objectives. That raises the need to define a national framework for the promotion and application of ICTs in the various production areas, particularly agricultural ones. It is for that reason that this study examined through qualitative methods (policy documents and semi-structured interviews) the national policy of Mali on the use of ICTs in agriculture. Data was analysed using the Qualitative Content Analysis (QCA) method with the aid of NVIVO 12 software. The results showed that the country has two policy documents that articulate the country’s strategy towards the use of ICTs in the agricultural sector, that is, the Agricultural Orientation Law and the National Strategy for the Development of the Digital Economy. Further examination revealed that that these two policy documents are neither appropriate nor coherent in today's Malian landscape. This has resulted in an underutilisation of digital tools by agricultural extension officers which led to the low agricultural productivity in the country. This study recommended therefore the recasting of both documents to take into account the reported observations
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".