Bridges between Wedges and Frames: Outreach and Compromise in American Political Discourse
Bibliographic record
Abstract
Wedges and frames, two much-studied strategies of American political combat, are generally thought to be partisan weapons, meant to manipulate voters into making trade-offs that favor the political actor wielding them. My inquiry here explores whether there exists anything comparably schematic to wedges and frames at work in attempts by American politicians not to polarize but to find consensus, not to cater to extremes but moderate them. Despite the seeming paucity of such efforts in American public discourse, there is one such common and as-yet untheorized scheme, which uses the two issue positions involved in wedges to overcome the ill effects of reframing and the two value dimensions involved in reframing to overcome the ill effects of wedges. I elaborate this discursive structure by examining its presence in a number of American political debates, showing how it differs from other contemporary normative-theoretic frameworks for understanding compromise in American politics.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.061 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".