Perceptions of Environmental Awareness and Pro-Environmental Behaviour of People in the Riverfront City, Pekanbaru City, Riau, Indonesia
Bibliographic record
Abstract
This research aims to analyze the perception of environmental awareness of the people of Kampung Baru, Kampung Bandar, Kampung Dalam, and Pesisir sub-districts, which is influenced by individual characteristics such as education and income.Then the perception of environmental awareness will influence the pro-environmental behavior of the people in these four sub-districts towards where they live.The area of these four sub-districts is the riverside city area of Pekanbaru City, which is starting to be organized into a tourist attraction known as Tepian Sungai Siak.The method used in this research was quantitative, distributing questionnaires to 120 respondents.The data was processed using SEM-PLS.From the results of research data processing, it is known that the respondents' characteristics, namely education and income, do not influence the respondents' environmental awareness perceptions.However, the respondents' perception of environmental awareness is classified as moderate or agrees with various management issues related to environmental concerns where they live, such as waste management, sanitation, clean water, energy, and concern for the surrounding environment.Furthermore, this environmentally conscious perception significantly influences the pro-environmental behavior of settlers on the banks of the Siak River.The results of this research can be used in designing government programs to create pro-environmental residential based on the pro-environmental behavior of settlers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".