Emotional geographies of roadkill: Stained experiences of tourism in Tasmania
Bibliographic record
Abstract
Abstract Globally, road fatalities affect wildlife populations and ecosystems, leading to ecological imbalances, economic losses, and safety hazards for both animals and humans. However, the emotional toll on humans is less well understood. This research explores tourists’ responses to roadkill, using emotional geography as the overarching framework, and focusing on the island state of Tasmania in Australia. Tasmania is known for its diverse and abundant native wildlife, as well as the unfortunate distinction of having Australia’s highest rate of wildlife fatalities caused by vehicle collisions, commonly referred to as roadkill. A mixed‐method questionnaire asked respondents to share emotions, and we then considered their relationships to socio‐demographic attributes. Around 97% of respondents encountered roadkill during their stays, and 63% encountered live animals on or near the road. Tourists identified sadness as the most felt emotion when confronted with the consequences of wildlife–vehicle collisions. Anger and disgust were also experienced, primarily because of the unpleasant sight of roadkill and the realisation that animals suffered. Women reported being more negatively affected than men. Tourists who had visited to see wildlife were more affected than those who had not. Analysis leads to the conclusion that unplanned, sporadic, unexpected, and confronting encounters with dead animals detract from the tourism experience for most, especially encounters with wildlife was anticipated as a positive experience on tour. Such findings have wider implications for those working in the tourism industry in mainland Australia, Canada, and South Africa, where roadkill is also problematic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".