Implementation of Land and Resource Management Plans in British Columbia: The Kamloops Experience
Bibliographic record
Abstract
Beginning in 1992, the British Columbia government has supported collaborative land and resource management planning processes throughout the province. These processes involve stakeholders from different sectors. The stakeholders develop land and resource management plans (LRMPs) with specific objectives and strategies. Several of the land use plans completed in the mid-1990s are now in the implementation phase and it is timely to evaluate implementation progress. This paper identifies factors believed to contribute to successful implementation from the literature. The relevance of these factors for collaborative plan implementation is tested the case of the Kamloops LRMP. The study reveals that an effective collaborative process, involving stakeholders, facilitates implementation even in the presence of a high complexity of problems, a diverse and large target group, and a large number of agencies involved. The study concludes with recommendations for the Kamloops LRMP and a checklist of factors for successful land use plan implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".