Outdoor recreation conflict on trails in Squamish, British Columbia : an examination of theoretical models and management.
Bibliographic record
Abstract
Understanding the social interactions of trail users is a challenge for recreation managers globally. Recreationists are not homogenous and differences in their expectations, behaviours and values may create conflict and consequently, recreationists´ levels of satisfaction may be decreased. On the one hand, numerous management practices have been suggested to deal with recreation conflict; of these, zoning and educational programs are two of the most commonly cited to address interpersonal and social values conflict respectively. However, the efficacy of these two approaches is still in doubt. Additionally, although past research found that many different attributes of recreationists are associated with conflict, no agreement has been reached on how to measure this conflict or even where the conflict comes from. On the other hand, several potential sources of conflict have been identified in literature, though they have not yet been included within the conflict models. Some authors have suggested that the underlying causes of conflict should first be identified in order for conflict management to be effective. This research is a survey-based, cross-sectional case study of trail users on nine areas of the Squamish trail network in British Columbia in which I 1) identify potential sources of conflicts among recreationists; 2) look for possible causal relationships between sources of conflict and the degrees of both the trail user´s activity style and resource specificity; and 3) asses the efficacy of two management practices (i.e., spatial zoning and educational signs) at reducing interpersonal and social values conflict respectively. Results indicate that spatial zoning and educational signs are useful at reducing interpersonal conflict and social values conflict, but only with regard to specific sources of conflict and at low effect sizes. Results also suggest that very different characteristics of recreationists significantly predict several sources of conflict. By investigating several characteristics of recreationists as well as their sensitivity to sources of conflict, this research has demonstrated that recreation conflict can be explained by different recreationist attributes, depending on the source of conflict under examination. The findings in this study lay the foundation for expanding further the existing conflict models in years to come.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".