Sustainability and Triple Bottom Line Planning in Social Enterprises: Developing the Guidelines for Social Entrepreneurs
Bibliographic record
Abstract
The article aims to discuss why and how the triple bottom line (TBL) approach can be adapted to manage the sustainability performance in social enterprises and thus assist the social entrepreneurs, who hold the central position in the process of social enterprise development. A system model based on design models such as the "Design of Results" and the "Cogniscope" was produced through the synthesis of multiple conceptual approaches following a systematic review protocol guided by the PRISMA Statement (‘‘Preferred Reporting Items for Systematic Reviews and Meta-Analyses’’). While extending the CogniScope' systems theory and practice in the context of S-ENT accountability, the article proposes the four phases (discovery, diagnosis and design, implementation, and measurement) for planning and organizing TBL efforts within social enterprises. The outcomes of the study will aid the S-ENT practitioners in the design and implementation of TBL framework in managing the sustainability performance of social entrepreneurship ventures. The applicability of the TBL approach can be explored and developed by subsequent work in different social entrepreneurship contexts.
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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 | 0.230 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".