The Effectiveness of Social Media Communications for Visitor Behaviour Management in Ontario's Parks and Protected Areas
Bibliographic record
Abstract
Parks and protected areas in Ontario have long been bastions of conservation while also providing critical outdoor recreation opportunities for the health and well-being of the people. This was particularly evident during the early stages of the COVID-19 pandemic, where parks and protected areas agencies around the world experienced drastic increases in visitation as people sought opportunities to spend time in the natural world. However, the balance of environmental conservation and the provision of outdoor recreation opportunities are often seen as competing interests given the potential degradation that is associated with human use of these natural spaces. As a result, it is crucial for park managers and protected areas agencies to mitigate negative visitor behaviour issues as much as possible. \n \nCommunications in their various formats (signage, in-person, etc.) 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, even less attention has been paid specifically to the utility of social media communications at delivering park agency messaging to visitors, especially in the context of addressing visitor behaviour issues using social media communications. This study will contribute to this identified 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. \n \nTo do so, this study applied interpretive description methodology (Thorne, 2016) to support semi-structured interviews with park visitors and individuals who work for park agencies in Ontario in park management roles. 17 participants participated in the research project throughout the course of the data collection process. \n \nConversations 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 strategy. Park visitors often expressed a desire for more specific, authentic, and discussion-oriented communications, while park managers frequently expressed a need to improve and increase the resources and logistics dedicated to social media communications to meet park visitor expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".