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Record W3117757764 · doi:10.15353/joci.v16i0.3489

An analysis of the policies of Information and Communication Technologies for Agriculture in Mali

2020· article· en· W3117757764 on OpenAlexvenueno aff
Macire Kante, Patrick Ndayizigamiye

Bibliographic record

VenueThe Journal of Community Informatics · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
FundersCentre National pour la Recherche Scientifique et Technique
KeywordsInformation and Communications TechnologyPromotion (chess)AgricultureBusinessICTSProductivityAgricultural productivityAgricultural communicationEconomic growthPublic relationsPolitical scienceMarketingEconomicsGeography

Abstract

fetched live from OpenAlex

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

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.269
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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