Dangerous Stories: Narrative Theory and Critique in a Post-Truth World
Bibliographic record
Abstract
Political and legal scholars use narrative theory to study everything from the framing of policy arguments to the telling of tort tales to the construction of political consciousness. Such scholarship often relies on post-positivist theories that problematize the empirical validity of narratives. But the stories told by many recent movements in American politics—such as Christian nationalism, “the Big Lie,” and Covid-19 conspiracy theories—so distort empirical reality that they endanger liberal norms and values, not to mention human lives. Scholars who ordinarily eschew objective narrative validity may nevertheless want to critique and challenge such stories on empirical grounds. This article investigates the options available to narrative scholars studying these types of stories. First, I survey different approaches to narrative, drawn from philosophy, rhetorical studies, critical feminist theory and critical race theory. Second, I highlight the resources and strategies devised by scholars who use these approaches to analyze other empirically problematic and socially dangerous narratives, especially how they have combined post-positivist commitments with concerns for truth and justice. Finally, I make suggestions for how scholars can better study and critique the political and legal narratives associated with the Trump era.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.021 | 0.125 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".