Off the Beaten Track: Messages as a Means of Reducing Social Trail Use at St. Lawrence Islands National Park
Bibliographic record
Abstract
During the 2004 visitor season, a covert observational study was conducted at St. Lawrence Islands National Park, Ontario, Canada to assess the effects of signs on mitigating social trail use on two of the park islands. Social trails are those not originally setup by park managers, but which arise due to off-trail use by visitors for a variety of purposes such as access to places of interest and shortcutting. In particularly sensitive or small island-based recreational areas, social trails can present significant disturbances to species at risk, and increase fragmentation of natural areas. The study examined the effectiveness of message text, and location in reducing the amount of social trail use by visitors. An attribution message was more effective than a plea message at eliciting desired behaviours. Furthermore, when signs were posted at social trailheads, use of the social trail was reduced significantly compared to no messages, or messages located at points of entry to the islands. Sign effectiveness is attributed to a message design which incorporated awareness, and internal locus of causality and control. National park managers could profitably implement attribution messages at appropriate locations to reduce social trail use specifically, and other forms of depreciative behaviour more generally. Plea messages, although eliciting significant reductions in social trail use, were not as effective. With levels of environmental concern in populations remaining positive over long periods, the use of messages that focus on personal responsibility and potentially encourage pro-environmental behaviour is proffered as an effective and economically efficient management approach.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".