Renewal and Resilience: the role of social innovation in building\ninstitutional resilience
Bibliographic record
Abstract
Society faces a number of ongoing and seemingly intractable problems -poverty, homelessness, environmental degradation, and disabilities among others -which governments and NGOs struggle to address.When efforts fail, it could be said that the system is caught in a trap, unable to respond to "chronic disasters" (Erikson, 1994) or immediate crises.On the other hand, social innovation, generally associated with creative initiatives on the part on one or many individuals, can at times transform such trapped systems.How and why does this happen?This abstract draws on a framework developed by a group of interdisciplinary scholars known as the Resilience Alliance (www.resalliance.org).This group, initially led and created by C.S Holling focuses on linked social and ecological resilience, defined as follows:Ecosystem resilience is the capacity of an ecosystem to tolerate disturbance without collapsing into a qualitatively different state that is controlled by a different set of processes.A resilient ecosystem can withstand shocks and rebuild itself when necessary.Resilience in social systems has the added capacity of humans to anticipate and plan for the future."Resilience" as applied to ecosystems, or to integrated systems of people and the natural environment, has three defining characteristics: * The amount of change the system can undergo and still retain the same controls on function and structure * The degree to which the system is capable of selforganization * The ability to build and increase the capacity for learning and adaptation This definition of resilience relies on a particular model of ongoing and dynamic change, called the "adaptive cycle" and the introduction of novelty through "bricolage" and through cross scale interactions across all phases of this adaptive cycle (Gunderson, Light and Holling, 1995; Gunderson and Holling, 2002).
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".