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
Climate change is no longer in the future, it is an evolving business and policy reality for tourism. Extreme weather events including heavy rainfall and flooding, drought, heat waves, storms, and wildfires have become more frequent and intense, affecting tourism destinations and demand everywhere in the world. Climate change also affects important tourism assets. Snowfall has become less reliable in many winter destinations, while sea level rise and ocean warming threaten resources such as beaches and coral reefs. There is also a rising cost of travel associated with climate change. All have in common that they will increasingly affect the global geography of travel and tourism. This paper provides an overview of the history of research into tourism and climate change, current research trends, as well as a discussion of key research gaps. It uses a geographical lens that centers on space, represented by destinations. Even though these interrelationships are now sufficiently well understood, there is limited evidence that industry or policy makers have internalized and act on this knowledge. Disruptions in tourism flows in time and space thus need to be anticipated.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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