Public deliberation for policy development using the community-informatics paradigm
Bibliographic record
Abstract
Public participation in decision-making is becoming an important area of research and practice. Citizens have expressed frustration that they lack the ability to influence policies, legislation, and practices that affect their lives directly. Decision-makers in public and private institutions and organizations have responded in measured and effective ways - measured in that public involvement opportunities must be well-researched and tested; effective in that people must perceive that they are heard and that their views have been taken into account, and ultimately effective in that decision-making is facilitated and enhanced. Pioneers in the use of public- deliberation methods in communities include Porto Alegre in Brazil and the Citizens' Assembly on Electoral Reform in British Columbia, Canada. This paper assesses a public-involvement process designed to support the development and approval of an urban food policy in a major Canadian city. Public deliberation in small citizen groups combined with social media tools were used to support the development of policy recommendations. The characteristics of public deliberation in comparison with other public consultations and discourses will be considered in the paper. Unlike other forms of public participation, public deliberation is characterized by an intentional articulation with change in public policy. In contrast with many forms of public consultation, public deliberation requires a systematic selection of participants to achieve a reliable representation of society. Public deliberation calls for a formal distribution of speaking opportunities, which is in contrast with the practices associated with public forums. Processes of public deliberation emphasize mutual respect among participants and between the citizenry and legislators, which can be difficult to achieve in less systematic modes of public consultation. Private or public broadcasters are commonly engaged in a long-term, collaborative relationship with organizers of public-deliberation events to broadcast proceedings and disseminate information about outcomes, in order to assist citizens in learning about and contributing to public policy. The requirements for a legitimate and effective public-deliberation process will be assessed in the context of the project. Lessons learned will be identified for extending public-deliberation methods in communities in Canada and internationally, using the paradigm and network of community informatics theory, methods, and practices.
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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 | 0.045 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".