Rethinking innovation labs for complex adaptive systems going through release and reorganization
Bibliographic record
Abstract
When working within a complex adaptive system going through the release and reorganization phase of the adaptive cycle (Holling, 2001) how should innovation lab practices shift? \n \nWe are exploring this question through an in-progress innovation lab facilitated by the Waterloo Institute for Social Innovation and Resilience. The Legacy Leadership Lab (L3) is situated at the intersection of the Canadian small business, social finance, and co-operative and social enterprise systems. The lab launched in 2019 and will continue into 2021 to explore systemic support for conversions of Canadian small- and medium-sized businesses into social purpose organizations. Originally, we intended to organize a variety of actors through a series of workshops to design initiatives that could be piloted by their organizations and communities. These plans were disrupted by the COVID-19 pandemic, which has not only foreclosed the possibility of gathering in-person but, more fundamentally, has radically transformed economic and social systems. These systemic changes have provoked us to rethink the lab’s focus and processes. Using Holling’s (2001) adaptive cycle, we suggest that the systems we work within are now in the release and reorganization phase (the “back loop”), out of the conservation phase where they had been for some time (see Figure 1). This abstract outlines how we are shifting our lab practices for the changing systems we find ourselves in. \n \nInnovation labs go by many names: social innovation labs (Westley et al., 2015), change labs (Westley, Geobey, & Robinson, 2012), social labs (Hassan, 2014), living labs, systemic innovation labs (Zivkovic, 2018), among others. These emerge from distinct communities, and practices vary between and even within each of these lineages (Kieboom, 2014). In essence, however, they share similar foundations. Labs are “container[s] for social experimentation, with a team, a process and space to support social innovation on a systemic level” (Kieboom, 2014). They draw on participatory design, design thinking, and systems thinking, and involve creating space for dialogue and sensemaking while stewarding a group of diverse stakeholders through a systemic design process (Tiesinga & Berkhout, 2014).
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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 | 0.066 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.042 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".