Bibliographic record
Abstract
This article offers theory-informed, learning-oriented, and imaginative insights into working in and with the unique stuckness of public sector organizations when trying to generate and catalyse transformative innovations on complex challenges. Imagining and enacting systems transformation in the public sector is transdisciplinary, creative, often subversive, and definitely daunting. We focus here on the Two Loops Model as a helpful archetype, a theory of change, and a creative prompt for systems transformation. Unlike many other models of transformation that are ultimately oriented toward finding and scaling solutions, Two Loops shows the dominant and emergent systems in an oscillating dance with a clear space between. We found this space to be an overlooked and potent place of praxis in our work, perhaps particularly so in the public sector, which tends to perpetuate the dominant system even when “innovating.” In this article, we dive deeply into this space to see what new and different perspectives it offers when working on complex challenges. We draw upon Black, Indigenous, queer, feminist, and decolonial scholars to help us think more deeply into this space, which is variously described as fugitive, wayward, hospice, Trickster, break, refusal, and snap. We then engage with this thinking in our own practice space—a public sector innovation lab inside local government. We visualize nine different views into and from this potent space-in-between and how we worked in, with, and from these views in our practice. Using engaged theory, reflective practice, images, metaphor, and poetic language, we aim to open up different possibilities for transformation efforts in the public and other sectors. We invite you to join us as we dwell in the messy, ambiguous, inner and outer work in this space, where we grapple with what we might need to do less of, and what we may need to do more of, in our efforts to move away from the dominant what is, and toward the emergent and resurgent what must be/come.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.091 |
| Scholarly communication | 0.023 | 0.032 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".