Putting the “Social” Back in Social Enterprise: An Evidence-Based Approach
Bibliographic record
Abstract
Published May 17, 2022 in Nonprofit Quarterly (NPQ) https://nonprofitquarterly.org/ Social enterprises are eager to foster a socially-conscious image while making a profit. These businesses are built around the idea that they can make money while also contributing to society. They are not alone: conventional businesses like Amazon and Toyota also strive to project a socially-conscious image. This causes a problem for entrepreneurs who may want to start a truly social business, and socially-conscious consumers. This article introduces a research-based framework that consumers and entrepreneurs can use to evaluate social enterprises. A lack of evidence-based planning is a shortcoming of many enterprises’ social business models. Social ratings systems such as ESG ratings generally evaluate investment risk, rather than rigorously measured social benefits, and certifications like b-corp are weak, their methodology secretive. This research framework can help entrepreneurs to build truly social businesses, and help consumers, journalists, and librarians investigate and evaluate businesses around them, and make evidence-based choices.
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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 | 0.262 | 0.490 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.031 | 0.016 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".