A qualitative narrative policy framework? <i>Examining the policy narratives of US campaign finance regulatory reform</i>
Bibliographic record
Abstract
In 2010, the narrative policy framework was introduced as a positivist, quantitative, and structuralist approach to the study of policy narratives. Deviating from this central tenet of the narrative policy framework, in this article we show that the framework is quite compatible with qualitative methods—and the various epistemologies associated with them. To demonstrate compatibility between qualitative methods and the Narrative Policy Framework, we apply classic qualitative criteria to an illustrative case examining policy narratives in US campaign finance reform. Drawing on elite interviews, we illuminate competing policy narratives rooted in distinct democratic values that exhibit variation in how victims and harm are defined, how blame is attributed to villains, what policy solutions are put forth, and policy narrative communication strategies. Our incorporation of qualitative methods within the narrative policy framework is critical for the framework's overall development as it provides opportunities for more detailed description, inductive forms of inquiry, and grounded theory development in policy areas where sample sizes, access, and salience may limit quantitative approaches.
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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 | 0.061 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".