Bibliographic record
Abstract
This paper build on previous research conducted by scholars at the Waterloo Institute of Social Innovation and Resilience on eight historical cases of social innovation, including the establishment of National Parks, the evolution of intelligence testing, the legalization of birth control, Indigenous legal recognition, the development of the internet, financial market innovations, Indian Residential Schools, and Dutch joint-stock companies. By applying the lens of complexity theory to these cases, we identify patterns in transformative innovation we draw out lessons about how social innovation can respond to “grand challenges.” We identify common patterns in successful, transformative social innovations such as high sensitivity to initial starting conditions, cyclical fluctuations, the importance of paradox and several others. Although our patterns were initially drawn from historical cases, we also point out resonances with the innovations discussed in this volume. These patterns suggest that we need to think differently about how we identify, support, and evaluate social innovations to better promote those with truly transformative potential. In line with this, the paper provides suggestions for evaluating the transformative potential of emerging social innovations, advocating for long-term assessment beyond immediate impact. We also caution that social innovation can contain “shadow” unintended consequences that may create new social problems, as seen in intelligence testing’s link to eugenics or the exploitation within residential schools. Overall, social innovation needs to be viewed holistically, with a long-term view, and to be treated carefully, if it is to unlock its potential as a force for positive transformation.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.093 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".