Bearing the Heat : A Serious Game on Cryotourism and the Climate Crisis
Bibliographic record
Abstract
With the ongoing climate crisis affecting every aspect of the cryosphere, landscapes arechanging, local communities are being forced to change their lifestyles, and the habits oftourists visiting these areas are also being affected. The tourist gaze, combined with the anxietythat tourists develop about the uncertainty of whether the destinations they want to visit willstill exist in the future, is causing the phenomenon of last chance tourism. In this climate ofuncertainty, tourists often travel by means that are harmful to the environment and areresponsible for carbon dioxide emissions. This contributes to further burdening the cryospherein the long term, fueling a vicious cycle. One of the areas plagued by this paradox of last chancetourism is the “capital” of polar bear viewing tourism, Churchill (Canada). The sea ice, onwhich polar bears depend for their survival, is decreasing due to global warming, causing bearsto spend more time on land, reducing their chances of survival. This phenomenon raisesdiscussions about environmental communication and sustainable tourism. At the same time,many methods can be used to communicate this paradox and its importance to the world. Oneof them is serious games. This thesis attempts to develop a serious game that will raiseawareness about the impacts of climate change and the paradox of last-chance tourism on polarbear habitats. This game should be as realistic as possible, in order to create an experientialexperience for the player. To achieve this, this thesis combines the processing of geodata froma GIS environment and its integration into a Unity graphics engine. In addition to the realisticenvironment, first-person camera, controls, navigation mechanics, and additional gamecomponents were added. The final product is a downloadable serious game that allows theplayer to explore a realistic representation of Churchill (Canada) and experience the paradoxof last-chance tourism through the eyes of a tourist. The goal of the game is to raise awarenessof the effects of climate change on polar bears and their natural environment. The gameincludes mechanisms for capturing photos and interacting with the environment. The game’snarrative enhances the educational value of the game. The experience is completed with a ratinglink, through which players can express whether the game strengthened their environmentalawareness. This game, as well as the research that accompanies it, can be a trigger for furtherdevelopment and applications in the field of environmental education and geospatialstorytelling through serious games.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".