Conflict management in BC provincial parks: a case study of mountain biking in Garibaldi Park
Bibliographic record
Abstract
At the same time resources are becoming more scarce there is an increase in the use of parks, wilderness and other natural environments for recreational purposes. This is evident in British Columbia where much of provincial parks planning is concerned with the accommodation of an ever increasing diversity of outdoor recreation activities. For a variety of reasons the people engaged in those activities do not always get along therefore resolving these social conflicts is becoming an ever larger part of recreation resource planners’ and managers’ jobs. The problem with conflict management in outdoor recreation is that the methods which are commonly used do not address the sources of conflict. Even though it is the recreationists who are experiencing conflict the focus remains on managing the resource. The traditional conflict management prescription is to separate activities that are considered to be incompatible. This action is necessary in some cases but it can often exacerbate the conflict. Since the reasons for conflict are largely sociological and psychological it is necessary that the groups in conflict get together to find a solution. Conflict management methods based on the spatial separation of activities that do not include this step will not be as effective as those that do. This thesis establishes a number of weaknesses in activity based conflict prevention by examining both the sources of conflict in outdoor recreation and the methods of conflict management which are traditionally used. These weaknesses are then considered in a two part examination. First, the conflict management policies of BC Parks are examined. Second, a closer look is taken at a specific conflict issue: the Garibaldi Master Plan and its treatment of the issue of mountain biking in the park. In general, without a formal conflict management policy in place users of BC’S provincial parks who find themselves in conflict with other users can not be assured that the sources of conflict will be addressed. In the Garibaldi Park case study, BC Parks focused on managing the resource rather than managing the social conflict that was occurring. They took steps in the right direction but failed to take the most crucial step which is getting the parties in conflict talking to each other. Even though the sources of conflict are recognized they are not the prime consideration in resolving the conflict.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".