Policy Assemblages and Policy Resilience: Lessons for Non-Design from Evolutionary Governance Theory
Bibliographic record
Abstract
Evolutionary governance theory (EGT) provides a basis for holistically analyzing the shifting contexts and dynamics of policymaking in settings with functional differentiation and complex subsystems. Policy assemblages, as mixes of policy tools and goals, are an appropriate unit of analysis for EGT because they embody the theory’s emphasis on co-evolving elements within policy systems. In rational practice, policymakers design policies within assemblages by establishing objectives, collecting information, comparing options, strategizing implementation, and selecting instruments. However, as EGT implies, this logical progression does not always materialize so tidily—some policies emerge from carefully considered blueprints while others evolve from muddled processes, laissez faire happenstance, or happy accident. Products of the latter often include loosely steered, unmoored, and ‘non-designed’ path dependencies that confound linear logic and are understudied in the policy literature. There exists the need for a more intricate analytical vocabulary to describe this underexplored ‘chaotic’ end of the policy design spectrum, as conjuring images of ‘muddles’ or ‘messes’ has exhausted its usefulness. This article introduces a novel metaphor for non-design—the bird nest—to bring studies of policy design and non-design into lexical harmony.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.056 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".