Exploratory multiple case study on successful upcycling businesses: ChopValue, Freitag and Pentatonic
Bibliographic record
Abstract
Upcycling has become an important concept and practice in creative industries for artists, craftspeople, designers and makers as evidenced by increasing number of practitioners and businesses in the UK and beyond, especially under the COVID-19 pandemic situation. Despite the potential, many upcycling businesses are often craft-oriented and small-scale with financial struggles, which calls for scaling-up in order to overcome such liabilities. This paper provides multiple exploratory case studies with successful upcycling businesses to shed light on how small, craft-oriented upcycling businesses could be scaled up to have bigger impact on the environment and society while being financially viable. Three successful upcycling businesses were selected: (i) ChopValue in Canada; (ii) Freitag in Switzerland; and (iii) Pentatonic in the UK. Online contents (official websites, news and magazines, and other media information) were qualitatively analysed using grounded thematic analysis. The results revealed five common themes as success factors across the three case studies: (i) sustainability values or philosophy at the centre of the business; (ii) product and service offers competitive on the market; (iii) partnerships/collaborations for business effectiveness; (iv) innovation in production for competitive edge; and (v) sustainable/circular supply chain management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".