The Adaptive Cycle: A Model of the Evolution of Social Innovations for Wicked Problems
Bibliographic record
Abstract
Confronting “wicked problems” such as climate change and persistent inequality requires more than isolated innovations; it necessitates instead a broad, often unpredictable confluence of efforts to generate significant impact. Prevailing models, rooted primarily in technological innovations, adeptly track how social innovations gain traction but fall short in capturing their interactions with entrenched evolutionary patterns. This essay advocates for the adaptive cycle as a means to bridge this gap, highlighting resilience as a key concept. In ecosystems, resilience explains regeneration potential, such as the ability of a lake to recover from a chemical spill. When applied to wicked problems, it reveals the role of regeneration dynamics in hindering but potentially also fueling the transformative potential of social innovations. We illustrate this through the history of sheltered workshops for disabled persons, which began in the 1840s and gained widespread influence a century later, transforming disability employment. The adaptations that facilitated the diffusion of workshops stifled their impact yet also spurred new cycles of social innovation. Embracing the adaptive cycle underscores the importance of resilience in understanding the impact of social innovations and articulates the cyclical nature of the evolutionary dynamics of stability and upheaval that define efforts to tackle wicked problems.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".