Social Media Communications for Behavior Management in Ontario’s Parks and Protected Areas: A Qualitative Analysis of Manager and Visitor Experiences
Bibliographic record
Abstract
Communications in their various formats have long been utilized by park agencies to share safety, regulatory, and interpretive information with park visitors. While the study of these communications is an underserved field of research in itself, less attention has been paid specifically to the utility of social media communications in the context of addressing visitor behaviour issues. This study addresses this research gap by exploring the experiences of both park visitors and park managers with respect to the effectiveness of social media communications for park visitor behaviour management. To do so, this study applied interpretive description methodology to support semi-structured interviews with park visitors (9) and individuals who work for park agencies in Ontario in park management roles (8). As management implications, interviews with participants revealed that the utility of social media communications for visitor behaviour management varies widely depending on the sophistication of the park agency’s social media approach. Park visitors often expressed a desire for more authentic and discussion-oriented communications, while park managers frequently expressed a need to improve and increase the resources dedicated to social media communications to meet park visitor expectations. Most park visitors and park managers interviewed reported social media can be an effective visitor management tool but that limits to its efficacy exist. Participants, while believing social media to be effective, expressed the belief that many of the individuals responsible for depreciative visitor behavior, are unlikely to be engaged with parks and protected area social media channels.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".