LOOKING FORWARD, PUSHING BACK AND PEERING SIDEWAYS: ANALYZING THE SUSTAINABILITY OF INDUSTRIAL SYMBIOSIS
Bibliographic record
Abstract
This paper compares and contrasts two different forms of interorganizational relationships that deal with the production and movement of waste: industrial symbiosis and supply chains. Industrial symbiosis reuses, recycles and reprocesses byproducts and intermediates within the system of organizations, whereas conventional supply chains reduce waste within manufacturing processes and reuse end‐of‐life products. Although both these models address waste, there is surprisingly little consideration of industrial symbiosis within supply chain research. Yet, industrial symbiosis has much to offer the study of sustainable development within supply chains. Industrial symbiosis emphasizes community, cooperation and coordination among firms, which serves to protect the environmental integrity, social equity and economic prosperity of the region — all hallmarks of sustainable development. However, such tight integration among a diverse set of organizations is difficult to jump start and difficult to maintain. In this paper, we also outline the challenges and offer some ideas on how to address these challenges. We ground our insights from interviews with firms in the Sarnia‐Lambton region of Ontario, Canada. This region is home to over 130,000 people, and has a strong physical infrastructure and social structures that have facilitated symbiotic relationships among local businesses.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".