The Dark Side of Community Dynamics in Organizations’ Pursuit of Social Innovation
Bibliographic record
Abstract
While recently scholarly inquiry has been foundational in our understanding of how social innovation can be influential in tackling some of society’s most deeply-rooted problems, recent years have witnessed a burgeoning interest in moving beyond focusing on organizations’ isolated actions towards an understanding of how communities may be centrally implicated in social innovation. Such perspective matters, as many social innovations are created in locally embedded contexts. Yet, what is surprising is that beyond the successful cases of community involvement, little has been said about the potential ‘dark side’ of communities and community dynamics in organizations’ pursuit of social innovation. Indeed, most of the social innovation literature to date has focused on the positive impact of good intentions by community actors, leaving the unintended consequences and the potential challenges of community dynamics in the organizational pursuit of social innovation underexplored. In this symposium we bring together four experts to discuss this matter in detail and provide an outlook for future research.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.004 |
| 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".