Bibliographic record
Abstract
There are long traditions of innovation in social policy and rather more recent traditions of policies for social innovation. The first category includes the creation of comprehensive welfare states and social services to support families, the elderly or children in need. The second category includes support for new fields of finance and investment for SI; experimentation, including randomised control trials, to test novel ideas in social policy; deeper integration of civil society and social movements into policy making; and the emergence of new subfields such as digital SI or civic tech, or social innovation in the context of the circular economy. Different countries have developed more coherent SI policies using widely divergent methods - from South Korea and Canada to Slovenia, Croatia, Taiwan and Sweden. This chapter provides an overview of some of these tendencies, and the dilemmas they bring with them, ranging from President Obama’s creation of an Office for Social Innovation to the European Union’s many programmes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".