Transforming the Business School Ethos Through the Teaching of a “Strong” Social Innovation: Pedagogical Opportunities and Tensions
Bibliographic record
Abstract
Over the past 15 years, social innovation (SI) has gained ground as a promising approach for tackling today’s grand challenges. A “weak” conception of SI focuses on improving how social needs are addressed through new products, services, technologies, business models, or practices. In contrast, a “strong” conception emphasizes deep social transformation and the empowerment of historically marginalized groups. In management education, SI has predominantly been taught through the lens of the weak conception. This paper explores the pedagogical opportunities and challenges of teaching a strong SI to business school students. We conducted a qualitative study of six courses that emphasize a strong SI, drawing on semi-structured interviews with both instructors and students. Our findings reveal the transformative potential of these courses, suggesting they can better prepare students to tackle today’s complex challenges by reshaping the traditional ethos of business schools. However, teaching a strong SI also requires instructors to navigate several key tensions related to action, organizations, management, emotional engagement with the world, and consideration of values and politics. These tensions offer a pedagogical map not only for courses centered on a strong SI but also for those adopting a critical approach to management and business organizations.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".