Probing Consumer Awareness & Barriers Towards Consumer Social Responsibility: A Novel Sustainable Development Approach
Bibliographic record
Abstract
Global scenario has revealed paucity of consumers’ awareness towards their social responsibilities which is certainly crucial to step forward on path of sustainable development. Regulating corporates practices has practically struggled for resource wastage reduction, dipping environmental harm, and improving societal welfare. Only corporates efforts are insufficient for desired improvement of societal & environmental health and demand consumers understanding their responsible actions towards society & environment. Centre of attention is to identify relationship between Consumer Social Responsibility (CnSR) and Corporate Social Responsibility (CSR). This relationship is endeavored by means of relationship matrix in which four unique developments are considered and accordingly solutions are suggested. Existence of barriers hindering consumers green decisions have been observed and addressed for better understanding. A total of 600 structured questionnaires were distributed, of which 457 returned and included incomplete contents also. Analysis is finally executed using 429 complete responses. Results are utilized to overcome barriers and improvise the consumers’ awareness towards various antecedents of CnSR for required sustainable consumption. Consumers’ requires support towards sustainable choices and as their awareness level will increase, they will start understanding the implications of their consumption choices leading to long-term sustainable practices.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".