Educational outcomes of commercial grizzly bear viewing participants : a case study from Port Hardy, British Columbia, Canada
Bibliographic record
Abstract
Commercial bear-viewing is a growing tourism attraction across North America, predominantly in Alaska, USA and British Columbia, Canada. Commercial bear-viewing can be understood as the phenomena of individuals, typically tourists or visitors paying a commercial operator for a guided, bear-viewing service including transportation, interpretation, and safety management. This study aimed to evaluate the learning outcomes and overall effectiveness of guided interpretation in commercially guided bear-viewing tours based from the town of Port Hardy, British Columbia, Canada. This study used a paired survey methodology to examine any changes in commercial bear-viewing participants correct, objective knowledge of responsible bear-viewing practices and their perceptions of the commercial bear-viewing industry before and after participating in a commercial bear-viewing tour. The results demonstrate a statistically significant improvement in participants correct, objective knowledge, as well as a statistically significant shift in respondents’ perceptions of the industry evidenced by both descriptive and inferential statistics. While there remains ample opportunity for further research, this study highlights the efficacy of interpretation, education, and knowledge in the context of commercial bear-viewing and improving both guest and bear safety.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".