Shifting Mindsets for Managing Complexity: A Municipal Case Study
Bibliographic record
Abstract
At the core of this research are the City of Kitchener’s Corporate Leadership Team (CLT) and selected management staff. These administrative City leaders engaged with REFOCUS, a Canadian cooperative non-profit organization, in applying the organizational change approach Enterprise Evolution (EE) to the municipal context, a global first. Enterprise Evolution is a methodology for co-creatively working with organizational leaders to build their system leadership capacity with innovative methods for managing for complexity. The first step in the EE programming is a series of workshops intended to lead to a mindset change among leaders by developing a more holistic understanding of emergent forces of change and appreciating the risks associated with maintaining the status quo. The EE theory of change defines this as an important step before engaging in a longer-term coproduction process of adapting strategic management practices. The focus of this study was to understand this initial process, its effectiveness within the specific context of the City of Kitchener, and its impact with the corporate leadership team. While there has been much theorizing about the need for a mindset change for deep and meaningful system change, empirical research reveals few successful, practical approaches that facilitate mindset change.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".