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Record W2018281232 · doi:10.5539/ass.v9n9p70

Problems in Using Agricultural Information from Television among Farmers in Malaysia

2013· article· en· W2018281232 on OpenAlexvenueno aff
Nor Sabila Ramli, Md. Salleh Hassan, Bahaman Abu Samah, Muhamad Sham Shahkat Ali, Hayrol Azril Mohamed Shaffril, Zoheir Sabagpour Azaharian

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersUniversiti Putra Malaysia
KeywordsAgricultureModernization theoryInformation and Communications TechnologyPreferenceProcess (computing)BusinessMarketingEconomic growthPolitical scienceComputer scienceGeographyEconomics

Abstract

fetched live from OpenAlex

Agriculture today must meet the demands of modernization, which include creating productive and innovative products. The success of this sector is aided by the evolution of technologies such as Information Communication Technology (ICT). Though television is categorized as traditional media, it is still considered an essential component of ICT and has been proven to play a crucial role in agricultural development, particularly on its extension process. This paper focuses on the problems faced by the agriculture community in receiving relevant information through television. This is a quantitative study which uses a questionnaire to obtain the data needed. A total of 400 respondents among farmers from four selected states in Peninsular Malaysia were surveyed. The data reveals that the main problems faced are few opportunities to watch television, short air times, and the fact that agriculture programs are not the farmers’ main preference in terms of what to watch. Based on the results, a related discussion is conducted and recommendations highlighted; these can assist concerned parties in strengthening their extension strategies.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0000.000
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 head0.020
GPT teacher head0.242
Teacher spread0.222 · 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 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

Citations8
Published2013
Admission routes1
Has abstractyes

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