Ideas and Discourse: Reform and Resistance in the Canadian and German Health Systems
Bibliographic record
Abstract
This article examines the conditions under which policy discourses can serve as contributing factors to policy change, even in the absence of changes in institutions and interests. It begins with a discussion of the role of ideas in policy analysis and how they can play a "constitutive role" as frames for policy. Drawing on a distinction between "augmentative" discourses that serve to reinforce an existing policy framework and "transformative discourses" that seek to persuade various publics of the need for significant policy change, four types of policy discourse are defined and a methodology is suggested for identifying these types. Two of these types, "challenging" and "truth-seeking," are hypothesized to be more conducive to the occurrence of significant policy change. Drawing then on case studies of policy change in Canada and Germany respectively, the article shows that a "challenging" discourse emerges in both countries, but leads to significant policy change only in Germany. Based on the comparison of the two cases, it is argued that three factors are relevant to whether a challenging discourse is successful or not: a broad consensus among core policy actors on the nature and gravity of the policy problem; the consistency of the discourse with broadly held normative values; and the persuasiveness of the "social facts" brought to bear in favour of proposed new solutions.
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.
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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.040 | 0.083 |
| Scholarly communication | 0.025 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".