Bibliographic record
Abstract
To address increasingly complex social and environmental problems, various sectors are leveraging social innovation–processes and practices that offer new ways of tackling social problems (Bacq & Janssen, 2011; Tracey & Stott, 2017). This process has given rise to hybrid forms of organizing, such as social enterprises that combine social and business goals (Battilana & Lee, 2014). While leaders play a critical role in enabling the success of social enterprises (Battilana & Dorado, 2010; Smith & Besharov, forthcoming; Wry & York, 2017) and are often seen as the critical factor in enabling social innovation in a variety of contexts (Bacq & Janssen, 2011), we know very little about how they successfully enact social change. To address this gap, our symposium examines the role of leadership to support social innovation. The symposium brings together four rich, in-depth studies exploring leaders’ sensemaking and decision- making in social-change contexts, as well as their impact on employees, the organizations, and the broader field. These studies explore social innovation leadership from a varied set of theories including paradox, sensemaking, and institutional theory, while drawing on diverse contexts including a rural redevelopment organization in Newfoundland, Canada, ‘community anchor organizations’ in Scotland, corporate philanthropy in France, and a social enterprise conglomerate in the U.S. Our discussant, Katherine Klein, will offer insights that explore intersections, divergences, and future research questions across these papers, while also drawing from her expertise in leadership research and role as Vice-Dean of the Wharton Social Impact Initiative. Inviting Engagement with Paradox: How Leaders of Social Enterprises Communicate Complexity Presenter: Natalie Slawinski; Memorial U. of Newfoundland Presenter: Wendy K. Smith; U. of Delaware Leading urgent acts of categorization: The construction of community anchor organizations Presenter: Neil Stott; Cambridge Judge Business School Presenter: Michelle Fava; Anglia Ruskin U. Presenter: Paul Tracey; U. of Cambridge Presenter: Laura Claus; U. of Cambridge Making Change Happen: The institutionalization of Corporate Philanthropy in France Presenter: Arthur Gautier; ESSEC Business School Presenter: Anne-Claire Pache; ESSEC Business School Presenter: Marion Ligonie; IESEG School of Management Presenter: Imran Chowdhury; Pace U. Institutional leadership in social enterprises: Integrating moral values and business Presenter: Tracy A Thompson; U. of Washington, Tacoma Presenter: Marya Besharov; Cornell U. Presenter: Gervase R. Bushe; Simon Fraser U. Presenter: Christopher D. Zatzick; Simon Fraser U.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.125 | 0.046 |
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".