Attitude: A Determinant of Agricultural Graduates’ Participation in Videoconferencing Technology
Bibliographic record
Abstract
The study envisaged to evaluate the attitude of agricultural graduates towards Videoconferencing (VC) technology. Study focused at persuasion stage of adoption process of innovations. A Likert-type-scale was developed, which consisted of 23-items. The scale instrument had four sections, viz., training, distance learning, agricultural communication and extension management. Cronbach’s alpha coefficient (?=0.85) of reliability test was measured. Instrument was administered to randomly selected, seventy agricultural graduates of Punjab Agricultural University (PAU), Ludhiana, India during 2006-07 academic years. Attitude survey proved insightful with agricultural students. They had positive attitude in applied areas of VC technology, viz. training, distance learning, agricultural communication and extension management. Significant difference between users and non-users of VC technology was observed. Users had significant positive attitude towards VC technology. An enhanced understanding of attitudes is imperative before effective intervention strategies to moderate attitudes for enhancing acceptance and implementation of VC technology. Including transfer of agricultural innovations, VC can be encouraged in services like training, distance education, extension management, communication, administration, health, education and knowledge sharing. For these, infrastructure available within National Agricultural Research System (NARS), India can be effectively utilized for learning and technical counseling. This study adds value to the body of knowledge in evaluation and theory building from attitude perspective. As standard methodology is lacking to comprehend mindset of agricultural students, it will serve foundation for other future investigations.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".