Direction for interpretive programming from Alberta Provincial Park management plans
Bibliographic record
Abstract
Park management plans provide strategic direction for the future management of specific parks. These plans set goals and strategies for many park management concerns, including ecological integrity, visitor services, facilities, boundaries, and resource allocation. Understanding interpretive goals, topics, and strategies will help a park or park system develop a coherent approach to interpretive planning, delivery, and evaluation. This study determined how interpretation was prioritized in Alberta provincial parks’ management plans. We analyzed 32 management plans based on length (average of 80 pages), age (average of 14 years), goals, topics, and strategies. Overall, 84% of the plans addressed interpretation, devoting an average of 3% of their length to interpretation. The most targeted interpretive goals were “learning,” “increasing positive attitudes,” “behavior change,” and “enjoyment.” The most frequent interpretive topics were “heritage,” “culture,” “conservation,” and “flora or fauna.” The most common interpretive strategies were “signs,” “general personal interpretation,” and “guided hikes.” Even though interpretation received a low emphasis, newer plans provided more emphasis, expanding on conceptualizing and evaluating interpretation compared with older plans. By summarizing the priorities of management plans for interpretation, this study may help park staff set interpretive goals, evaluate progress, and promote consistency between the goals of park staff and outcomes for visitors. In turn, this information may help park planners and practitioners to better align interpretive goals, strategies, and outcomes.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".