Narrative policy framework at the macro level—cultural theory-based beliefs, science-based narrative strategies, and their uptake in the Canadian policy process for genetically modified salmon
Bibliographic record
Abstract
This study utilizes the Narrative Policy Framework (NPF) and cultural theory to examine the use of policy narratives by coalitions (meso-level) and the institutional uptake (macro-level). We analyze Parliamentary hearings about genetically modified (GM) salmon in Canada to associate narrative strategies with certain cultural worldviews and policy-stances. We explore narrative strategies used by cultural groups with regard to whether they contain the scope of GM salmon issues to “science-only” (direct health and environmental impacts) or expand the issues to “science-plus” (to include broader economic, social, or cultural impacts). Finally, we examine whether certain framings of GM salmon issues or specific cultural narratives are preferentially taken up in the final policy documents generated after the hearings. Our findings reveal significant relationships between policy-stance (pro-vs anti-GM), the cultural disposition of a policy narrative, the narrative strategies being used, and ultimately policy uptake. For example, narratives with hierarchical cultural dispositions were more likely to expand the scope of the issue to science-plus when supporting their own policy position (typically pro-GM) but contain the scope to “science-only” when refuting an anti-GM policy-stance. With regard to policy uptake, final government documents referred more to narratives that contained the scope to “science-only” and expressed hierarchical or individualistic dispositions in comparison to the hearings. This study has practical implications for understanding whose perspectives and arguments are legitimized in national policy debates about GM foods. It also extends NPF theory to how narratives containing specific cultural dispositions and risk-based framings influence policy uptake at the macro-level.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".