Encouraging Public Involvement in Public Policymaking through University-Government Collaboration
Bibliographic record
Abstract
New methods of involving large numbers of citizens in public decision-making using information and communications technologies have spurred academic and professional interest. This chapter will describe the case of the Citizen Panel, a public-involvement project in which a municipal government and university combined their capacities to create a significant new opportunity for public involvement in public policymaking. Technology was used to broaden access to participation in, and awareness of, the Citizen Panel. Technology application included development of a video version of the information resources used by the Citizen Panel, posting key information on the website, hosting a Facebook group discussion, and live broadcast of panel sessions by Web streaming. The Citizen Panel provided a “proof of concept” for the subsequent establishment of the Centre for Public Involvement, which is a partnership between the municipal government and the university. The Centre for Public Involvement’s purpose will be to engage in research and development in support of improved public-involvement practices and processes.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| 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".