Fostering the Growth of the Social Impact Business sector in Viet Nam
Bibliographic record
Abstract
“Fostering the Growth of the Social Impact Business Sector in Viet Nam” (the “study”) is the largest study on the social impact business (SIB) sector in Viet Nam undertaken to date. Findings are taken from 492 survey responses, interviews with 62 individuals representing various stakeholder groups, through one-on-one interviews or focus groups, and three multi-stakeholder consultative workshops. The study provides an overview of the ecosystem and the current state of the SIB sector in Viet Nam, together with challenges and opportunities, to produce key recommendations to grow the sector. Practical guidance targeted at SIBs in growing their enterprise is also included to share the advices and insights taken from consultation with SIB sector leaders. The main objective of the study is to catalyse the development of business activities toward addressing pressing social and environmental challenges, and ultimately toward the achievement of the UN Sustainable Development Goals (SDGs)1. The study was conducted in the context where there is a need to map the SIB sector in Viet Nam, which is recognised as an invaluable driver for positive social and environmental change, in order to design interventions to support the Government of Viet Nam in achieving the SDGs by 2030. The study aims to apply a wider understanding of the SIB sector to map and understand the huge potential of the sector from across a varied spectrum of organisations and models, all connected by their mission to solve social or environmental issues. For the purposes of the study, SIBs are understood as “organisations that have both trading activities and a commitment to positively impacting society/environment as the two central tenets of their strategic operations. This balancing of their social/environmental aims with a commercial model allows them to sustainably solve social and environmental challenges.”. This sector may include non-profit organisations having commercial activities, legally registered Social Enterprises (SEs), cooperatives, inclusive businesses, social impact startups, and commercial enterprises for sustainable development. SIB in this study is therefore provided as a working definition, one that seeks to respond to the concerns and barriers facing business leaders who have integrated social missions into their business models, and hence foster further growth of the wider SIB sector in Viet Nam.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".