Social Entrepreneurs with Disabilities: Exploring Motivational and Attitudinal Factors
Bibliographic record
Abstract
The current economic climate demands more innovative approaches to increasing labor market participation for people with disabilities. Social entrepreneurship offers one alternative pathway to employment. However, little is known about the motivational and attitudinal factors influencing social entrepreneurship for people with disabilities. Using empirical data from focus groups comprised of social entrepreneurs with disabilities, and interviews with key stakeholders working in the fields of policy, disability, and business, this research frames its analysis in the intersection of disability studies and entrepreneurial studies to explore: what motivates people with disabilities to pursue social entrepreneurship, if they continue to encounter attitudinal barriers and discrimination, and whether motivational and attitudinal factors affect their social entrepreneurship. Findings indicate that despite social entrepreneurship having been promoted as a strategy for circumventing employment discrimination, the individuals with disabilities in this research continued to encounter attitudinal barriers and discrimination affecting their employment decisions. Future research should focus on interrogating what might be gained in the spaces where need and opportunity intersect and exploring the extent to which motivations overlap for social entrepreneurs with disabilities in theory, policy, and practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".