"What Works Well; What Needs Improvement": Lessons in public consultation from British Columbia's resource planning processes
Bibliographic record
Abstract
Resource planning and management in British Columbia, Canada, has been steadily moving towards more active public participation. While government agencies have long been required to consult the general public during the course of land or resource use planning, the 1990s brought in a period of more intense public involvement. In terms of resource planning, this led to the creation of several new planning processes. Given that there is now considerable experience with the Commission on Resources and Environment (CORE) and the Land and Resource Management Plan (LRMP) processes, it is time for an appraisal. In particular, the paper examines the public's perceptions of these processes with respect to 'what works well' and 'what needs improvement'. The results highlight a number of areas to which process designers and managers should direct attention. There are three key items of note. First, there are generally low levels of awareness by respondents of public consultation processes in their community. Second, there is a need for access to timely, relevant and readable information throughout the course of the process in order to keep participants and the public as up-to-date as possible. Finally, there must be greater clarity about the process itself, including mandates, participants and decision-making powers.
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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 | 0.039 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.053 | 0.023 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".