Bibliographic record
Abstract
Winning and losing is a prominent feature of politics in Congress: an institution where decisions are determined by counting votes. In the politics of Congress, the more important concern is whether decisions are intelligent in the view of the circumstances of policy and the goals and interests relevant to a decision. Such intelligence depends on the manner Congress uses information and reasoning in making decisions. Simply put, intelligence depends on the quality of effectiveness of deliberation. While there has been vast research on Congress, most of it has been focused entirely on influence, coalitions, and other issues of winning and losing. Much of these research has overlooked deliberation and the intelligence of decisions. This neglect on the issue of deliberation is unfortunate as intelligent decision-making is rather difficult for Congress and stakes are generally high. Luckily, there has been an increasing interest in the process and problems of deliberation in Congress. This article reviews the development of literature that focuses on the issues of deliberation in Congress. It focuses on the conflicts and ambiguities, including advances in the literature of deliberation in Congress. It also offers some suggestions about promising directions for future work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".