Social and Digital Media Utilization by NGO’s for Uplifting Farming Community in UT of Puducherry
Bibliographic record
Abstract
Social and digital media facilitates effective communication by sharing ideas, thoughts and information through virtual networks among rural communities. Social media use by the farmers is inevitable and enhances them to interact with their neighboring community, officials, market, and other development agencies. NGOs, a major player in rural development and serves the objective to work with the community are harnessing the benefits of social and digital media by establishing strong relations with people and organizations. NGO’s explores various dimensions of social and digital media to interact with their beneficiaries. With this background, a study was conducted to explore the social and digital media initiatives for rural development with the dimensions of social and digital media utilization pattern, preferences of beneficiaries, perception about social media, type of information sought by rural people. The study was conducted by analyzing the activities of three reputed NGOs, viz., DHAN, MSSRF, and CEAD functioning in the U.T of Puducherry. The officials and beneficiaries of the NGOs were examined for this analysis. The results show that most NGOs use social media to share technical information related to crop production/animals husbandry followed by marketing information, weather, and training related information. The officials of NGOs and beneficiaries perceived that these media are beneficial for rural development. The important barriers expressed by them include language of message, digital literacy, cost of access data and connectivity. The suggestion offered by them includes training in ICT, internet speed, proper translation of messages in local language. The acceptance towards social and digital media is positive to a considerable utilization from the NGOs and farmers. The respondents admitted that social and digital are effectively transfer the information and technology, easy to operate, cover large number of farmers, ensures timeliness in access to send and receive information, an effective mechanism to manage the dearth of staff in NGO’s and cost effective. At contrast due to the education level of farmer/rural people, income, experience in handling ICT tools, social and digital media failed to be an effective teaching tool, convince rural people towards the information reliability, and reach farmers without discrimination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".