Grassroots Innovation Movements
Bibliographic record
Abstract
Innovation is increasingly invoked by policy elites and business leaders as vital for tackling global challenges like sustainable development. Often overlooked, however, is the fact that networks of community groups, activists, and researchers have been innovating grassroots solutions for social justice and environmental sustainability for decades. Unencumbered by disciplinary boundaries, policy silos, or institutional logics, these ‘grassroots innovation movements’ identify issues and questions neglected by formal science, technology and innovation organizations. Grassroots solutions arise in unconventional settings through unusual combinations of people, ideas and tools. This book examines six diverse grassroots innovation movements in India, South America and Europe, situating them in their particular dynamic historical contexts. Analysis explains why each movement frames innovation and development differently, resulting in a variety of strategies. The book explores the spaces where each of these movements have grown, or attempted to do so. It critically examines the pathways they have developed for grassroots innovation and the challenges and limitations confronting their approaches. With mounting pressure for social justice in an increasingly unequal world, policy makers are exploring how to foster more inclusive innovation. In this context grassroots experiences take on added significance. This book provides timely and relevant ideas, analysis and recommendations for activists, policy-makers, students and scholars interested in encounters between innovation, development and social movements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".