Classifying Social Enterprises Through Theoretical Typologies to Understand Social Innovation
Bibliographic record
Abstract
In the latter, there has been an increasing importance attributed to the measurement of social value and social impact that various organizations create. The demand to measure this value comes from all sides: funders who want to direct their money to the most effective projects, policy makers and government officials must be accountable for their spending decisions, and social organizations need to demonstrate their impact to financiers, partners and beneficiaries. This article intends to classify social companies through theoretical types and analyze their characteristics to understand social innovation. The first stage of the project was the elaboration of the theoretical framework on the themes of social enterprise, typology of social enterprises, social business model and social innovation. The research instrument was an interview guide, and the next step was to select three social companies of different types to carry out the empirical research. These typologies were tested in three social companies in the empirical research and the effectiveness of the typologies was proven.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| 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".