Half a century of “muddling”: Are we there yet?
Bibliographic record
Abstract
Abstract Half a century after the publication of Lindblom's seminal article “The Science of Muddling Through”, we revisit the heritage of incrementalism in this special issue, analyzing its legacy in public policy and public administration. The articles discuss the extent to which recent theoretical developments have transformed the original idea, reinforced it, or possibly rendered it obsolete. In this introductory article, we provide a short overview over the core elements of incrementalism and assess how the concept is used in scholarly publications and research today. We thereby focus on incrementalism as an analytical concept rather then a prescriptive theory. We argue that even after a half a century of “muddling”, we are not yet through with incrementalism. Some of the ideas that underpin the concept of incrementalism continue to drive research, often in combination with more recent theoretical approaches to the policy process. After half a century, incrementalism is still part of the policy scholar's tool kit.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".