Community Inclusion under Systemic Inequality: How For‐Profit Businesses Pursue Social Purpose
Bibliographic record
Abstract
Abstract In countries where systemic inequality is pervasive, purposeful businesses that assume wider societal responsibilities try to counteract its effects by including marginalized social groups in their value creation processes. While current research documents a variety of business approaches for community inclusion, the nature, drivers and effectiveness of these inclusionary practices are not fully understood. We develop and empirically validate a framework of community inclusion that explicates the mechanisms through which purposeful businesses generate civic wealth – or economic and social benefits – to disadvantaged community groups. We differentiate between commercial practices that recast existent firm‐centric processes towards creating value for marginalized groups and collaborative practices that aim to devise novel, participatory processes for engaging marginalized groups. Analysis of primary data from a sample of 430 small businesses in seven African countries confirms that the effect of social purpose on civic wealth is partially mediated by the two inclusionary practices. Businesses are more likely to extend the scope of their inclusion through collaborative practices when they receive favourable external validation and when institutional voids are low. We contribute to the literature by documenting the role of social purpose in motivating the pursuit of community‐level goals and by unpacking the specific inclusionary practices used to achieve them.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".