What makes public engagement successful? Evaluating public engagement in the Humber Valley Regional Planning Project
Bibliographic record
Abstract
The current research was an investigation of successful public engagement in land use planning, using a case study from Newfoundland and Labrador. The research involved two dimensions: 1) the evaluation of a 14 month-long public engagement process for the development of a regional plan, taking place in the Humber Valley, NL, and 2) the development of a list of criteria for successful public engagement in land use planning, particularly in the Humber Valley. A total of 275 regional residents participated in this research through a regional survey, evaluation questionnaires, and interviews. The data was collected based on performance indicators, which were used to evaluate the engagement process. A set of criteria of successful public engagement from the literature were informed by the current research to include: Openness, Representativeness, Appropriate Timing, Careful Preparation, Broad Advertising, Comprehensive Information, Influence on Decision-Making, and Empowerment. The Humber Valley project was also evaluated based on these criteria. The findings suggest some key areas, particularly associated with openness, advertising, and information, which may be important focal points when developing and implementing public engagements projects.
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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 | 0.049 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".