Start-ups within entrepreneurial ecosystems: Transition towards a circular economy
Bibliographic record
Abstract
This article explores the role of start-ups within entrepreneurial ecosystems in driving the transition towards a circular economy. It emphasises the importance of understanding and supporting circular start-ups for broader sustainability impacts. Unlike established firms, start-ups can readily adopt ambitious circular business models (CBMs) without the risk of business model cannibalisation and with the agility to adapt to market trends. CBMs enhance value creation, delivery and capture resource flows in an optimised non-linear fashion. Scaling up CBMs is crucial for overall economic, social and environmental benefits. Hence, leveraging the key entrepreneurial ecosystems actors, such as universities, business incubators and related venture development intermediaries, is vital for start-up support. In this special issue, we have invited researchers to submit contributions that delve into the dynamics among start-ups, entrepreneurial ecosystems and the circular economy, aiming to enrich our understanding of the early stage start-up development process with the aim of promoting the circular economy at a firm, regional or national level.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".