Modes and orientations of intermediation and championing: Advancing the circular transition in the textile industry
Bibliographic record
Abstract
The essential transition from linear supply chains to circular ecosystems in the textile and fashion industries creates challenges regarding collaboration and knowledge exchange across previously disconnected sectors. In other sustainability transitions contexts, scholars have recognized the importance of intermediaries and champions for addressing related challenges. This paper investigates a European circular textile ecosystem that extends beyond single, linear supply chains, focusing on how intermediaries and champions engage in collaboration to advance circular textile development. We analyze their engagement under four modes, namely brokering, configuring, facilitating and capacitating, and structural negotiating, and further clarify differences in intermediaries’ and champions’ orientations toward such engagement. The paper provides insights into how intermediation and championing can strengthen cross-sectoral, circular textile ecosystem collaboration and promote a broader circular transition in the field. Knowledge on intermediation and championing is crucial for accelerating circularity in the textile sector, as various EU policies push toward rapid change.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".