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Record W2549327557 · doi:10.1080/23738871.2016.1249898

Exploring the multi-stakeholder experience in Kenya

2016· article· en· W2549327557 on OpenAlexfundno aff
Alice Wanjira Munyua

Bibliographic record

VenueJournal of Cyber Policy · 2016
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsInformation and Communications TechnologyStakeholderStakeholder analysisGeneral partnershipPoliticsBusinessPublic relationsStakeholder managementOrder (exchange)Knowledge managementPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This paper attempts to explore the extent to which the multi-stakeholder model has contributed to the vibrant information and communications technology (ICT) sector in Kenya. It shows how stakeholder organisation and lobbying, as well as political decisions, influenced the innovation and diffusion of ICTs, and how the multi-stakeholder approach gained support from both governmental and non-governmental players in the ICT sector. The paper highlights socio-political dynamics and changes that have taken place, which have led to the multi-stakeholder approach being applied in order to develop ICT policies and create new institutions deploying infrastructure, rollout, and management. It also explores how the multi-stakeholder partnership (MSP) approach has been applied to the discussion and regulation of post-access issues. This paper is based on interviews, the author’s broad experience in this area, and reviews of Kenya ICT Action Network documents, as well as mailing list discussions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0330.011
Scholarly communication0.0050.009
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.173
GPT teacher head0.317
Teacher spread0.143 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2016
Admission routes1
Has abstractyes

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