Whose input counts? Evaluating the process and outcomes of public consultation through the BC Water Act Modernization
Bibliographic record
Abstract
Public consultation has become an increasingly common form of democratic engagement. While critics have challenged the potential for public consultation to democratize policy-making due to existing power structures, few studies have undertaken a systematic evaluation of the policy outcomes of consultation. This study combines qualitative and quantitative techniques to systematically analyze participants’ responses to policy proposals, and compare those responses with resulting policies. We utilized this approach to examine the large-scale public consultation process that informed the development of British Columbia’s new Water Sustainability Act (2014). Our analysis revealed: (1) barriers to effectual engagement, particularly for First Nations; (2) statistical differences in policy preferences between industry and nonindustry groups; and (3) patterns in how these preferences align with policy outcomes, suggesting uneven participant influence on policy-making. This study highlights the importance of analyzing consultation outcomes alongside process design, and the need to assess consultation’s fairness and effectiveness by examining its outcomes for different participant groups.
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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 | 0.145 | 0.349 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".