Social and Digital Media Utilization by NGO’s for Uplifting Farming Community in UT of Puducherry
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».