MétaCan
Menu
Back to cohort
Record W2532802186 · doi:10.1080/08941920.2016.1239148

“There is No Program Without Farmers”: Interactive Radio for Forest Landscape Restoration in Mount Elgon Region, Uganda

2016· article· en· W2532802186 on OpenAlexaff
Karen J. Hampson, Mark S. LeClair, Askebir Gebru, Lynne Nakabugo, Chris Huggins

Bibliographic record

VenueSociety & Natural Resources · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
Fundersnot available
KeywordsCitizen journalismCommunity radioEnvironmental planningGovernment (linguistics)AgricultureEnvironmental resource managementBusinessGeographyPolitical sciencePublic relationsEnvironmental science

Abstract

fetched live from OpenAlex

Increase in ownership and use of radios and mobile phones in Uganda may present opportunities for interactive and efficient agricultural extension services. Yet the impact of interactive radio on rural development has rarely been evaluated. In a participatory project, the International Union for the Conservation of Nature together with Farm Radio International and stakeholders from local government, radio, and community-based organizations promoted the increased use of forest landscape restoration activities though interactive rural radio programs, including innovative methods to reach populations not covered by radio. An evaluation found that 98% of people who listened to most or all of the broadcasts carried out one of the practices, whereas 84% of those who had listened to only one episode had used one of the practices. Inclusion of a wide range of community stakeholders from project inception was vital for presenting the project and embedding it within local institutional contexts.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueSociety & Natural ResourcesSame topicAgricultural Innovations and PracticesFrench-language works237,207