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Record W1975633225 · doi:10.6000/1927-5129.2015.11.42

Study of the Mobile Phone Technology in Creating Awareness among Small Farmers of Sindh Province

2015· article· en· W1975633225 on OpenAlexvenueno aff
Har Bakhsh Makhijani, Muhammad Ismail Kumbhar, Shuhabuddin Mughal, Hameeda Masood Shah, Naseer Ahmed Abbasi

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2015
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsMobile phoneAgricultureBusinessProduct (mathematics)OfficerMobile technologyThe InternetPhoneMarketingAdvertisingSocioeconomicsGeographyEngineeringSociologyTelecommunicationsMobile computingComputer scienceMathematics

Abstract

fetched live from OpenAlex

Information and communication knowledge have played a positive role in different segments of the society such as in agriculture education and community development. Now a days most of the farmers are using these technologies especially mobile phones which have given a fruitful result to community. This study was conducted in Sindh Pakistan and survey was conducted in district Jamshoro Taulka Manjhand. Total two hundred respondents were randomly selected for data collection. The study indicated that 90% of the respondents possessed their personal mobile phones and 70% of the respondents utilized mobile for communication with their friends. While 75% of the were of the opinion asked that mobile phones have made their lives easy. However, the results showed that 72% did not contact with any agriculture officer and similarly not contacted with customers to sell their product. Furthermore, study revealed that 74.5% of the respondents replied that they utilize internet on mobile phones. Overall result indicated that farmers are not getting any benefit or increase their income, save time and energy by using the mobile phones in their places.

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)0.003
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 score0.590
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.047
GPT teacher head0.277
Teacher spread0.230 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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