A Field Survey on Social Responsibility Practices Carried Out by Textile Companies in Turkey Within the Scope of Sustainable Development
Bibliographic record
Abstract
Sustainable development is a process of change that involves institutional restructuring related with directing investments, positioning technological development and meeting the future needs as much as today’s needs. The application of environmental social responsibility studies is useful for any size of textile company. A primary concern for textile companies is the amount of water used in their processes. They generate large volumes of solid and hazardous wastes. Energy is the motor behind textile processes. Energy costs used in processes are important for especially large textile companies. While institutions are producing with social responsibility awareness, fulfilling their obligations to environment has gained importance in terms of sustainable development. The subject of this work introduces activities carried out by textile companies during production process, fulfilling their environmental responsibilities or not and which applications are done in the sector. Following the literature survey, a questionnaire study was conducted on textile firms operating in Turkey. From our research, it is concluded that some emerging technologies like enzymatic treatment in textile wet processing, ultrasonic treatment, electron-beam treatment, use of supercritical carbon dioxide in dyeing, electrochemical dyeing, ink-jet printing, plasma technology in textile wet processing are not used by any of companies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".