Exploratory Study of Textile Undergraduates’ Knowledge and Perception towards Eco-Friendly Clothing in Bangladesh
Bibliographic record
Abstract
Environmentalism leads to the concept of eco-friendly clothing (EFC) and its popularity is advancing all over the world. In-depth knowledge acquisition regarding EFC has become a fundamental requirement for Bangladeshi Textile undergraduates as they are the future professionals in the EFC sector. To ascertain the knowledge level and perception of the Bangladeshi textile undergraduates regarding EFC was the aim of this study. In this exploratory study, a self-administered questionnaire was used to collect data through purposive sampling from the students enrolled into the Bangladesh undergraduate Textile Engineering programme. The respondents were 282 students of the fourth year of different universities located in Dhaka city. Descriptive statistics were used to represent the findings of the research. The results showed that 82.3% of the respondents were informed about EFC, 35.8% were knowledgeable regarding EFC raw materials and 53.02% were cognisant about the production process. 89.4% of the respondents expected one particular course on EFC in curriculum and 94% wanted to contribute towards EFC in the future. The study revealed that undergraduates have a knowledge gap regarding EFC, while their willingness to learn and contribute is very optimistic. The findings suggested that the evaluation and modification of the curriculum for EFC and incorporation of EFC courses can lessen this salient gap.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".