Emotional geographies of roadkill: Stained experiences of tourism in Tasmania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it