Rhetoric and Reality: Going beyond Discourse Ethics in Assessing Legislative Deliberation
Bibliographic record
Abstract
Political science has recently devoted some attention to the study of legislative deliberation. But it has reached no consensus about the basic concepts and approaches for investigating such deliberation. We identify four distinct normative perspectives in the legislative deliberation literature, and give particular attention to two of them—one that focuses on a debate's compliance with expectations of discourse ethics, and one that focuses on the substantive adequacy and intelligence of its consideration of policy issues. We consider the major comparative study by Steiner, Bachtiger, Sporndli and Steenbergen (2004) to clarify its relation to the various perspectives. We then discuss the challenges and possibilities for the perspective concerned directly with the intelligence of deliberation in more detail. In the largest part of this article, we provide an overview of our own work employing this perspective, presented in our recent book, Deliberative Choices: Debating Public Policy in Congress. Finally, we make brief comments on how our methods—and in contrast, those of Steiner, ea.—pertain to the performance of the lower chamber in a parliamentary democracy. In the end, we seek to vindicate the possibility and indeed centrality of an approach that assesses legislative deliberation primarily for its ability to deal accurately and intelligently with the realities of public policy.
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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 | 0.137 | 0.332 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".