What is Social Innovation and How is it Done?
Bibliographic record
Abstract
Every truth passes through three stages. First, it is ridiculed. Second, it is violently opposed. Finally, it is taken to be self-evident. Introduction The first two decades of the 21st century brought burgeoning interest in social innovation – its nature, needs, possibilities and dilemmas. There were new policies; funds; research studies; accelerators; emerging fields around design, the maker movement, open data and the sharing economy, climate transition and social justice; and action involving many hundreds of thousands of people, from Canada to China, Sweden to South Africa, with benefits reaching billions. This effervescence marked a shift in perception of both ends and means. It grew out of a recognition that too much innovation was being directed to the wrong ends – to warfare and killing; to the needs of the rich; or to trivial or harmful purposes. Too many of the world's most creative brains were working on the wrong tasks, while the world's most urgent needs were left underserved. Just as important was a shift in thinking about means: a recognition that innovation had become too focused on hardware and things, and that it was far too much an elite preoccupation, for the well-educated and well-connected in big cities, with far too little role for the rest in making and shaping. So, attention turned to how to make innovation more inclusive; how to tap into household innovators; civil society; and the creativity of communities. In both respects social innovation fed off a widespread desire of people to take more control of their lives and their futures and a dissatisfaction with existing institutions. As I show later in this book, this is a story in progress, and still in its early stages. But it has allowed us to see the past, the present and the future in a quite different light. The heritage of social innovation Much of what we take for granted in social life began as radical innovation, the work of dreamers not content just to dream. A century ago few believed that ordinary people could be trusted to drive cars at high speed; the idea of a national health service freely available to all was seen as absurdly utopian; the concept of a ‘kindergarten’ was still considered revolutionary; and in 1900 only one country had given women the vote.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".