Toward Collaborative Cross-Sector Business Models for Sustainability
Bibliographic record
Abstract
Sustainability challenges typically occur across sectoral boundaries, calling the state, market, and civil society to action. Although consensus exists on the merits of cross-sector collaboration, our understanding of whether and how it can create value for various, collaborating stakeholders is still limited. This special issue focuses on how new combined knowledge on cross-sector collaboration and business models for sustainability can inform the academic and practitioner debates about sustainability challenges and solutions. We discuss how cross-sector collaboration can play an important role for the transition to new and potentially sustainability-driven business models given that value creation, delivery, and capture of organizations are intimately related to the collaborative ties with their stakeholders. Sustainable alternatives to conventional business models tend to adopt a more holistic perspective of business by broadening the spectrum of solutions and stakeholders and, when aligned with cross-sector collaboration, can contribute new ways of addressing the wicked sustainability problems humanity faces.
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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 | 0.041 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.031 | 0.045 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".