Personal Interpretation Starts with the Interpreter: How Do Outcome Priorities Change Over Time among Interpretive Staff?
Bibliographic record
Abstract
Personal interpretation is a key management tool to help visitors discover, value, and enjoy parks. Goals and objectives for personal interpretation should be consistent among staff to ensure that planning and delivery are effective in achieving targeted outcomes. The aim of this study was to assess how the outcome priorities for, and resources used by, front-line interpreters (employees who deliver personal interpretation programs) changed over the course of one season. Staff goals are also influenced by agency goals and agency support. We were interested in how park agency legislation and management goals were aligned with front-line staff goals and how the agency supported (or did not support) staff in achieving those outcomes. Front-line interpreters from Alberta Parks completed in-person questionnaires during May 2018 (time 1), describing their priorities for interpretive outcomes, perceived trends, resources used, opportunities, barriers, and demographics. Following the delivery of personal interpretation in 11 provincial parks across Alberta during the May to September 2018 park visitation season, the same interpreters completed a follow-up survey between October 2018 and January 2019 (time 2) to understand if and why those responses changed. Respondents were asked to rate their priorities for six main outcomes of personal interpretation. The top outcomes at time 1 were visitor enjoyment, connections to place, and learning. At time 2, learning and enjoyment priorities declined; attitudes, behavioral change, connections to place, and positive memories did not change as desired outcomes. When asked which of the six outcomes were most important, respondents indicated connections to place, positive memories, and enjoyment most often (but there were no changes from time 1 to time 2). As for resources used in guiding content and strategic decisions in delivering personal interpretation programs, the importance of park legislation and park finances declined; the importance of the other resources did not change. When asked what organizational factors helped them be successful (or unsuccessful), staff relied overwhelmingly on their immediate team, including their supervisors and fellow interpreters. However, responses from time 2 suggested that respondents felt in need of more training, more time to prepare new and innovative programs, and more support from upper-level managers and their agency. This research can help inform park practitioner efforts to understand how interpretive priorities change over time and what resources are important for interpreters. Park agency managers can use these results to hire, train, and nurture front-line interpreters, with a goal of improving the impact of personal interpretation programs.
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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 | 0.027 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".