Level of Knowledge of Agricultural Science Graduate Students about Climate Change Mitigation and Adaptation Practices of Agriculture
Bibliographic record
Abstract
Climate change (CC) is a global environmental problem and source of concern. Effective planning and implementation of CC mitigation and adaptation may arise from knowledge of its causes and effects. Therefore, dissemination of knowledge is highly important for ensuring that the knowledge grows and spreads amongst the various stakeholders and that it is turned into action. The students of today are the leaders and policy makers of tomorrow. They will effectively serve as change agents once their knowledge base has been well established. This study provides analysis of graduate students' level of knowledge of CC, its nature, causes, effects, mitigation, and adaptation. The study population was 57 agricultural science master's students in the Faculty of Agricultural Sciences in Jarash University, Jordan, and the sample consisted of 50 of those students. The study used online test as the knowledge assessment and data collection tool. Frequency distribution analysis uncovered that the sample students possess high level of general knowledge of CC, moderate level of knowledge of mitigation of CC, and high level of knowledge of adaptation to CC. As to the three investigated facets of general knowledge of CC, these students have high levels of knowledge of the nature and the effects of CC and moderate level of knowledge of its causes. These findings contribute to understanding of students' knowledge achievements and gaps and of the need for curricular reform in terms of structure and content that can be shared by agricultural science faculties around the World with similar CC graduate programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.001 |
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".