Post-Consumer Textile Recycling: Challenges and Opportunities, Northern Periphery and Arctic Communities’ Perspective
Bibliographic record
Abstract
The study explores the circularity of textiles with a focus on reuse and recycling within the Northern Periphery and Arctic (NPA) communities.With a need to delimit the study, the Northern regions of Finland, Norway, Sweden, and Ireland were chosen as the areas of focus due to their proximity to one another and their shared demographical and geographical characteristics.As a result of the conducted research, the current textile waste recycling landscape in the NPA within the public sectors of the regions and technologies that would be commercially viable for the areas were identified and discussed.The current recycling landscape among the regions highlights the challenges the industry faces, the factors to consider when choosing a potentially viable textile waste recycling technology, and the current stance and updates regarding the Extended Producers Responsibility on textiles.Based on this data, the researchers suggests that mechanical recycling technologies would technically be suitable for the areas, but due to the lower-than-average textile waste volume generated per area, it may be recommended to have a shared or centralized recycling facility for these northern regions.
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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 | 0.003 | 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 | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".