Strategic policy narratives: A narrative policy study of the Columbia River Crossing
Bibliographic record
Abstract
This study examines how coalitions in local policy contexts implore policy narratives to expand or contain the scope of policy issues. The Narrative Policy Framework (NPF), a maturing theory of the policy process, is utilized in this study to conduct content analysis on 370 public documents from competing coalitions in relation to the Columbia River Crossing project; a “wicked” policy issue in the Portland, OR/Vancouver, WA region of the Pacific Northwest. From this case selection, it is hypothesized that competing coalitions will use narrative strategies of containment and expansion to achieve their desired policy outcomes. It is also theorized that shocks to a policy subsystem may result in a shift to coalitional narrative strategies. This research will shed light on how coalitions strategically implore policy narratives in cohesive and less cohesive ways to influence policy outcomes.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".