Bibliographic record
Abstract
Abstract Research indicates that polarization has led to an increasing dispersion between moderate and more extreme voters within both parties. Intraparty polarization supposedly affects the nature of interparty competition as it creates political space for new political realignments and the rise of anti-establishment candidates. This article examines the extent and impact of intraparty polarization in Congress on US trade policy. Specifically, the article examines whether (and which) trade policy preferences are distributed within and between both parties, as well as how intraparty polarization has influenced the outcome of US trade negotiations. It is theorized that intraparty polarization causes crosscutting legislative coalitions around specific trade policies and political realignments around ideological factions, with consequences for the outcome of trade negotiations. By relying on a unique dataset of congressional letters and co-sponsorship legislation, the article first derives trade policy preferences from members of Congress and computes their ideological means. Two contemporary cases of US trade policy are examined: The Transpacific Partnership Agreement and the US–Mexico–Canada Agreement. Via a structured-focused comparison of both cases, the paper finally assesses under which combinations of preference-based and ideology-based intraparty polarization Congress manages to ratify trade agreements. Findings suggest that both parties are intrinsically polarized between free trade and fair trade preferences yet show variance in their degree of ideology-based intraparty polarization. These findings contribute to existing work on bipartisanship as well as factions in the foreign policy realm, as it shows under which circumstances legislators can build crosscutting coalitions around foreign policies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".