Bibliographic record
Abstract
Issue redefinition and venue shopping have been identified as key strategies for enacting agenda and policy change, but much work remains to be done in elaborating these processes. I argue that an important aspect of issue redefinition involves shifting not only the image of an issue but also the bases for considering those issues—what I call policy principles. Policy principles are the core values, beliefs, or guidelines attached to policies that help direct decision making. The emergence and acceptance of new principles by the public and policymakers can be a vital source of policy change, at times having far greater consequences for policy than redefining an issue. Venue shopping is also a multifaceted undertaking involving efforts by policy entrepreneurs and advocacy groups to keep issues out of venues they would rather not participate in as well as move decision making to new arenas. Moreover, while the literature suggests that shifting venues is usually a sensible strategy, sometimes venue shopping can backfire. A case study of the municipal movement to restrict the nonessential use of lawn and garden pesticides in Canada illustrates these theoretical points and shows the applicability of agenda setting models to contexts outside the United States.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.055 | 0.038 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".