Perception of the Regional Political Leaders on Climate Change: A Study in Sylhet District in Bangladesh
Bibliographic record
Abstract
The paper has been designed to explore the perception of the regional political leaders on climate change. It explains the perception of the political leaders about general information, causes, and impacts of climate change. Therefore, the research design of the study was explorative and mostly descriptive. Probability sampling was used for sample selection. From the population of 367.58 respondents were selected by using Cochran’s formula. This study incorporated both primary and secondary sources of data. Data was collected through social survey and in-depth interview method from the study area. These pertinent data were analysed by different statistical method like percentage analysis, weighted mean index, chi-square method etc. It was found that most of the respondents’ perception about the climate change is satisfactory comparatively where perception about causes of climate change was better than the perception about impacts of climate change. Here, year of schooling was significantly associated with the respondents’ perception on climate change. On the other hand, involvement with voluntary organization is significantly associated with respondents’ perception about impacts of climate change. On the contrary, duration of the involvement with political party has no significant relation regarding to the perception of the political leaders on various aspects of climate change.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".