Still budgeting by muddling through: Why disjointed incrementalism lasts
Bibliographic record
Abstract
Abstract This paper examines Charles Lindblom’s ‘science of muddling through’ and its transport and transformation to budgeting through the work of Aaron Wildavsky by analyzing its impact on budget theory and assessing its continued relevance to the practice of budgeting in the context of the Canadian federal government. We find that: the concept is of fundamental importance and yet considerably elastic to capture key features of political, economic, and organizational life; the criticisms of it have been overstated although, perhaps surprisingly, with the exception of budgeting, little empirical testing of the concept has been undertaken; the impact on budget theory and practice has been considerable especially since much of the initial application was undertaken at a time of relative economic and political stability in government and in budgeting; and yet even today, in a more turbulent world, the concept remains surprisingly relevant, although not complete, for understanding and explaining some of the most central and enduring features of budgetary behaviour. It is a key to our understanding of how budget participants deal with complexity and manage conflict.
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".