The V formation model: a unifying force for double bottom line ventures illustrated with European and North American examples
Bibliographic record
Abstract
Purpose Both social entrepreneurship (SE) and corporate social responsibility (CSR) are explored as parts of the contemporary movement toward sustainable business practices. In particular, this paper aims to address some of the confusion with the emerging field of SE through an exploration of theoretical models and practical applications across contexts. Design/methodology/approach This article reviews an array of research that has focused on defining a continuum of social mandate across the for‐profit to non‐profit arenas. It further experiments with plotting examples from North America (Canada) and Europe (Croatia) to test the models' practical value. Findings There are many gradations but the basic elements of intention and implementation along the lines of double (mission and money) and triple (people, planet, profit) bottom lines are converging. As the SE movement gains momentum across the world both experts and those new to the field are in search of a common tool to aid in consensus building and development across borders and sectors. Research limitations/implications The V formation model emphasizes the importance of the starting point of a social organization in terms of whether it is rooted in charitable or business practices, before allowing for a more nuanced understanding of the depth and intensity of its commitments to balance at the V‐Point of symmetry. Originality/value The authors present their own conceptual model with ten mini case studies presenting a diverse spectrum of SE activity that supports an inclusive rather than exclusive view of the present and future of both social entrepreneurship and corporate social responsibility initiatives.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".