A Bridge in Flux: Narratives and the Policy Process in the Pacific Northwest
Bibliographic record
Abstract
Abstract Policy narratives play an important role in the policy process. Often policy narratives originate from advocacy coalitions seeking increased support from the public for their policy stance. Although most Narrative Policy Framework studies have focused on national policy issues, this study examines a state and local economic development project by exploring the policy narratives from competing coalitions in favor and opposed to the project. Specifically, in the Portland–Vancouver area of Oregon and Washington, local policy discussions have been dominated by a proposal for a new mega bridge on Interstate‐5 connecting the two cities across the Columbia River. A new government agency (CRC—Columbia River Crossing) was formed for the implementation of this project. Upon approval of a proposal, CRC experienced heavy backlash from citizens, local businesses, community leaders, and other stakeholders leading to the formation of two competing coalitions in opposition and support of the bridge. This study, using content analysis of 370 public documents, finds that competing coalitions utilize policy narratives in strategic ways to characterize the opposing coalition, themselves, and other actors in the policy subsystem. This study also suggests that the strength and cohesion of a coalition's narrative contributes to its policy success and the winning/losing status of a coalition potentially determines the types of strategies they will use. Last, this study introduces and tests a new narrative strategy called the impotent shift testing a coalition's strategic use of the victim character.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 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".