Strengthening democracy: The capacity of AI-powered insights for enhancing policy deliberation and transparency
Bibliographic record
Abstract
The global decline in democratic governance suggests that more innovative approaches are required if civic engagement in the process of policy development is to be enhanced or maintained. This research explores the role of generative artificial intelligence and natural language processing in this context through a case study of three Canadian parliamentary Bills: Bill C-12 (Canada Net-Zero Emissions Accountability Act), Bill S-211 (Fighting Against Forced Child Labour in Supply Chains Act), and Bill C-18 (Online News Act). Using Python-based tools and OpenAI’s 4o-mini, this study analyses transcripts of parliamentary debates to extract key argumentative themes and to generate policy recommendations. AI-generated recommendations are then compared with the actual content of the Bills, identifying areas of alignment, complementarity, and divergence. The findings demonstrate AI's potential to provide an analytical lens on legislative processes, surfacing underemphasised arguments and revealing alternative policy dimensions; aspects often absent in final legislation. Ultimately, this study underscores AI's capacity to augment, rather than replace, traditional governance methods, offering a pathway to strengthen the quality and transparency of democratic deliberation.
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".