Bi- Partisanship a Double-Edged Sword in Western Liberal Democratic Governance
Bibliographic record
Abstract
In 2023, the American Congress had one of the lowest bi-partisan voting scores in history, with over 80% of votes split along ideological lines. Political polarisation has paralyzed legislative enactments and has ruined the unity of western liberal democracies, therefore, a study into bipartisanship, its catalysts, and its determinants is essential to maintain our democratic institutions. Throughout this study, we study bipartisanship through a process of triangulation, using a mix of qualitative and quantitative approaches. This will include the use of voting records, polling, and survey data to seek out a measure of bipartisanship. We will use case studies and the rules of our institutions to explain why bi-partisanship has receded recently and then look at media and speech rhetoric to look at how bi-partisanship is framed in the public eye and their opinions on cross-party collaboration and its subsequent effect on bi-partisanship within legislative bodies. A series of factors can be attributed to the lack of bipartisanship in our deliberative chambers. Through studies held in the United States of America, findings and studies have concluded that a large reason for such political polarization is an assumed over exaggeration of policy differences, a re-alignment of party intentions, misinformation, fact-checking, and electoral incentives. On the contrary, aides for bi-partisanship can include such factors as: fact-based reporting, education initiatives, civil discourse, open primaries, ranked-choice voting, economic recovery acts, and external threats to a nation's sovereignty or freedoms. Findings on the factors affecting bipartisanship seem paradoxical. Whilst floor transparency increases partisan theatrics, open committee hearings tend to lower political posturing. Therefore, we intend to find out how to maximize cordiality, respect, and of course, bipartisanship between ideological lines.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".