Crisis, uncertainty and urgency: processes of learning and emulation in tax policy making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines how ideational factors shape policy making during crisis conditions. Crises can generate 'problem uncertainty', in which policymakers are uncertain about the nature of policy problems. Existing studies have linked such conditions to processes of policy learning. Yet crises can also trigger 'policy urgency', where policymakers' preference for immediate policy action is paramount. This study suggests that bounded emulation, in which policymakers copy available solutions without learning, is related to perceptions of policy urgency. To probe the plausibility of the framework the study conducts a comparative analysis of value-added tax reform in Ontario and British Columbia, drawing on 41 semi-structured interviews, policy documents and news articles. The study finds that high uncertainty and moderate urgency facilitated policy learning in Ontario, while moderate uncertainty and high urgency fostered bounded emulation in British Columbia. The article identifies the implications of the findings for future research on ideas and policy 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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it