Climate Change: A Review of Its Health Impact and Percieved Awareness by the Young Citizens
Bibliographic record
Abstract
In recent time climate change and its impact on human health and awareness constitute a set of complex and serious consequences to be tackled by an individual country. Climate change is not merely an environmental issue, but also it is a threat that goes beyond national borders. The purpose of this study is to identify the awareness and the impact of climate change, perceived by the young citizens in Malaysia by focusing on gender differences. Based on a survey of 200 respondents from different public and private University's students in Malaysia, this research used descriptive statistics and T-test to look into the research objective. The results revealed media can play an important role in the awareness of climate change. Meanwhile the male respondents have shown considerable attention on the physical impact of climate change like heat related stress. On the other hand female respondents have shown considerable attention to the psychological impact by the climate change. From a pragmatic perspective, the findings from this research will assists the policy makers to understand more about the perceived awareness on the climate change issues of the young citizens which ultimately assist them to inaugurate new initiatives to confront the challenges of climate changes. This research is among the pioneer study on the issue of the perceived awareness in regards to climate change in Malaysia by focusing on gender differences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".