Socially Inclusive Circular Economy Business Models and Ecosystems
Bibliographic record
Abstract
The Circular Economy (CE) can be a path to reducing both environmental deterioration and social exclusion. However, despite the increasing body of research investigating CE outcomes, there is little understanding of how to extend and embed social inclusion within emerging circular economy business ecosystems. Currently, our understanding of how to address these circular ecosystem challenges and deliver and scale circular economy benefits is obscured by the paucity of theoretical development in socially inclusive circular economy business models, the lack of confirmatory studies, and the dearth of literature exploring the lens of social justice and explicitly addressing social inclusion in CE. The purpose of this panel symposium is to explore and discuss theoretical and practical perspectives of CE business model ecosystems that involve socially inclusive approaches. The panel symposium will engage a group of panelists in formal, moderated, and interactive discussions around the central question of “how can the circular economy be scaled through ecosystem models that foster social inclusion?”. The panelists will critically explore (1) the interactions of multiple stakeholders, feedback loops and the emergence of complex behaviors among agents; (2) the extent to which social inclusion is embedded in circular ecosystems; (3) CE strategies that contribute to social inclusion; (4) how social inclusion and resource efficiency can be aligned; and (5) what factors influence such relations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 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".