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Record W2335433314 · doi:10.5367/oa.2011.0033

Evaluation of a Distance Education Radio Farm School Programme in India: Implications for Scaling up

2011· article· en· W2335433314 on OpenAlexfundno aff
P. V. K. Sasidhar, Murari Suvedi, K. Vijayaraghavan, Baldev Singh, Suresh Babu

Bibliographic record

VenueOutlook on Agriculture · 2011
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
FundersIndian Agricultural Research InstituteCollege of Engineering, Michigan State UniversityInternational Development Research Centre
KeywordsOutreachActive listeningDisseminationRadio programBusinessHierarchyMedical educationPsychologySocioeconomicsPolitical scienceEconomic growthSociologyComputer scienceTelecommunicationsMedicineEconomics

Abstract

fetched live from OpenAlex

Distance education radio programmes on poultry farming with registered participants were organized using the local language in an effort to link researchers to rural poultry farmers through radio broadcasts. This study, based on data from 74 participants and 60 non-participants, assesses the impact of the radio farm school on participants using Bennett's hierarchy. Information gathered from the participants included the inputs used, the production activities carried out, the outputs obtained and the reactions of the participants with respect to listening behaviour, opinions, knowledge, attitudes, adoption changes and SWOT parameters. Overall, the evaluation found that the farm school on radio with registered participants had a major impact on developing awareness, knowledge and changes in attitude and in involving end-users in outreach activities. The related implications for scaling up and harnessing the medium of radio to disseminate outreach information are discussed.

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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.044
GPT teacher head0.308
Teacher spread0.264 · 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 designObservational
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

Citations6
Published2011
Admission routes1
Has abstractyes

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