Bibliographic record
Abstract
Legislative debate serves as a critical mechanism for democratic governance, shaping political discourse, holding governments accountable, and representing diverse societal perspectives. Recent advancements in computational tools have enabled the processing and quantification of vast volumes of legislative debate, highlighting the need for robust measures of debate quality. However, such measures do not naturally emerge from the data. How should we assess the quality of debates in legislative assemblies? While existing frameworks for assessing debate quality – most notably the Discourse Quality Index (DQI) – are rooted in deliberative democratic ideals, they struggle to capture the adversarial nature and institutional realities of parliamentary discourse. This dissertation develops the Parliamentary Discourse Quality Index (PDQI), a novel framework designed specifically for evaluating the quality of legislative debate within parliamentary systems. Drawing on a comparative analysis of deliberative and parliamentary models, this study critiques the application of deliberative standards to parliamentary settings, arguing that majoritarian institutions function under distinct normative and institutional logics. Using Canada as a case study – where both adversarial and consensus-based parliamentary structures exist – this dissertation assess the DQI and demonstrates the need for an alternative evaluative framework. The PDQI introduces key indicators that align with the institutional objectives of parliamentary democracy, including clarity of scrutiny, responsiveness to accountability, engagement with political alternatives, respect for parliamentary norms, and the legitimating function of debate. By incorporating these dimensions, the PDQI offers a contextually grounded and empirically operational framework for assessing legislative discourse. This dissertation contributes to the study of political communication, legislative studies, and democratic theory by challenging the universal application of deliberative ideals to adversarial institutions. It underscores the importance of evaluating political discourse through institutionally appropriate frameworks and provides a methodological foundation for future research on parliamentary discourse.
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 | 0.014 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".