Role of Mass Media in Dissemination of Agricultural Technology among the Farmers of Jaffarabad District of Balochistan
Bibliographic record
Abstract
Agricultural extension is essentially a message delivery system organized to convey the latest findings ofagricultural research to farmers. Effective communication is therefore, the prime requirement in extension work. This study conducted during 2013 attempted to examine the role of mass media in dissemination of agricultural technology among the farmers of Jaffarabad district of Balochistan province of Pakistan. The results revealed majority of the respondents were male (80%), belonged to the age group of 31-40 years (45.35%), and with formal education of (31%). Information regarding agricultural farming revealed that three-fourth (75%) of the respondents owned personal land, medium size of farms (12.5-50.0 acres) were more common (52%). Majority (70.93%) of the respondents perceived that the sources of media used in the area are highly conventional. About two third (66.28%) of the respondents perceived that the sources of media for agricultural information was highly accessible. Relative majority of the respondents (40.70%) supposed to prefer listening to agricultural programs between 8 pm to 12.00 am; 33.72% respondents showed preference for listening to agricultural programs from 4.00 -8.00 pm. Majority (70.93%) of the respondents considered the information receiving through mass media is highly relevant in solving agriculture problems. Majority (41.86%) of the respondents reported infrastructural development due to agricultural information received through mass media and 22.09 percent found that agricultural information received through mass media was helpful in capacity building. Regarding major obstacles in receiving information, 31.40 percent respondents reported power failure, followed by high cost (24.42%), and poor signals (12.79%).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".