Bibliographic record
Abstract
Like other western liberal democracies, Canada has witnessed the erosion of political participation and civic engagement on the part of its citizens. Recent studies of Canadian democracy have revealed numerous symptoms of malaise, including declines in voter turnout, participation in traditional political institutions, civic literacy, and trust in government (Gidengil, Blais, Nevitte, & Nadeau, 2004; Nevitte, 1996). Governments at the federal, provincial, and municipal levels have launched numerous democratic reform initiatives in response. Along with proposals for electoral and parliamentary system reform, governments in Canada have responded with new citizen consultation initiatives designed to increase public participation in the policymaking process. Incorporating the use of new information and communication technologies (ICTs) into these initiatives, such as online citizen consultation tools, has become a common method used to engage Canadians in the policymaking process. A gradual shift in the language and practice of citizen involvement in the policymaking process has also been taking place, one in which citizen consultation is being complemented by richer and more sustained forms of citizen engagement. This chapter examines the political context and conceptual underpinnings of online citizen consultation and engagement in federal policymaking in Canada, reviews a number of recent examples, and assesses their outcomes in light of their potential to overcome the democratic malaise currently ailing Canada’s political system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".