Orchestrating Circularity within Industrial Ecosystems: Lessons from Iconic Cases in Three Different Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores how to get companies engaged in value-creating cooperation regarding residual materials. Within different contexts, industrial ecology needs matchmakers who act as network orchestrators to facilitate new forms of interorganizational cooperation on what were previously perceived as “junk materials.” Three case studies of eco-industrial networks in Denmark (Kalundborg), Canada (the Québec region), and France (Dunkirk) demonstrate the various roles of the matchmakers to ensure the implementation of industrial ecology at the interorganizational level. This article highlights four strategic activities for matchmakers: revealing value in industrial ecology, generating trust, activating industrial ecology, and institutionalizing industrial ecology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it