Resistance to change: A case study on framing and policy change of a controversial nature area
Bibliographic record
Abstract
Nature policies can be a major source of long-term debates, in which actors involved define problems differently and are unable to formulate (co-constructed) solutions. Especially issues about the well-being of animals raise heated debate among stakeholders. Though debates over nature policies often span longer periods, they are most likely dealt with on the short term. Policy makers will attempt to solve acute issues, which requires minimal political effort. However, these short-term solutions do not necessarily solve the issue as a whole. This paper analyzes conflicting frames about nature in the Dutch Oostervaardersplassen, and presents an analysis of how the different issues are debated, and framed, over a period of 23 years. Gaining in-depth insight into these frames shows linkages between media attention to issues and policy change. This research shows how diverse and unstable the debate has been over 23 years, by using Punctuated Equilibrium Theory to understand the policy process, and by analyzing the evolution of frames in the media with an Evolutionary Factor Analysis. With the combination of both Punctuated Equilibrium Theory and the Evolutionary Factor Analysis, we can relate issue framing to policy change. This shows that policy is adapted, following rising attention. However, at first the attempts for adaptation by policy makers will be minor, as stability is favored over change, until a certain threshold whereafter policy is changed radically. The article will provide more insight for stakeholders, scientists, and policy makers into the complexity of these kinds of wicked problems.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".