Analyzing Urban Communities Level of Environmental Awareness for a Future Sustainable Use of Plastic Packaging
Bibliographic record
Abstract
For the last decades, problems have occur due to the increased plastic used as packaging for various products.Plastic may be considered profitable for the company for their products because it is easy to use and has a low price which economically benefical for the company.However, problems may arise due to its nature of being hard to break, the material making it difficult to decompose, causing piles of plastic waste.Therefore, this research aims to determine the level of environmental awareness in individuals.We aim from the humanperspective since human are the subject of plastic users in daily life, as we access the level of knowledge, attitude, and behavior towards plastic use.We used a quantitative research method and distributed questionnaires to 268 respondents.Distributed frequency analysis was done using SPSS, and we categorized the total scores of each variable into two groups.Then, we composite the three variables to determine the level of environmental awareness.Results show that based on respondent's knowledge, attitude, and behavior towards plastic use, the majority of respondents, about 55.6% have good environmental awareness, whereas the other 44.4% have poor environmental awareness.This result indicates that the respondents already have formed their awareness, which may be beneficial in forming their pro-environmental behavior in future daily life, therefor aiming for future sustainable behavior of plastic use, especially plastic for packaging.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".