Exploring the potential and limits of social enterprise as a path to addressing structural injustice
Bibliographic record
Abstract
This study considers how Social Enterprises (SEs) contribute to addressing structural injustice faced by people in marginalized groups, identifies the basic needs met by SEs and the supports SEs need to make broader social impacts. Exploring the potentials and limitations of SEs also reveals the ways that the state can support and initiate local development by SEs and other community development organizations. By developing a Peace and Conflict Studies (PACS) influenced understanding of the ‘social’ in social enterprise, this analysis also explores the link between peacebuilding and development studies, which scholars in the field often do not consider beyond post-peace accord environments. These research objectives were accomplished through the analysis and interpretation of data about the Manitoba SE sector drawn from multiple sources, although the major focus was on the qualitative data. Twenty SE managers and business developers in Winnipeg participated in an interview for this study. This study also carried out a descriptive, exploratory analysis of data from a survey conducted by the Mino Bimaadiziwin partnership team with 169 individuals entering Work Integration Social Enterprises (WISEs) in First Nation and non-First Nation communities near Winnipeg. Secondary sources like the Manitoba Social Enterprise Strategy (MSES) and reports on SEs in Manitoba are also used in this study. The study produced four key findings. First, Work Integrated Social Enterprises (WISEs) contribute to positive peacebuilding by meeting their employees’ economic needs and facilitating their socioeconomic inclusion if appropriately designed to accomplish these objectives. Second, SEs contribute to the eventual transformation of the inequitable socio-economic structure of society irrespective of whether they frame their work as transformative or compensatory. Third, SEs must collaborate with other SEs, government, community development organizations and even for-profit businesses to expand their social impacts. Fourth, that SEs require supports from the public, for-profit and non-profit sectors to expand their social impacts. Such supports can range from providing SEs with supported income (whether in the form of funding, grants or payment for social outcomes) and advocating for them, to promote an interest in SEs through education and developing a separate legal designation for SEs.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| 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".