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 Tourism is a major global economic sector that is undergoing tremendous growth in emerging economies and is often touted as salient for development and poverty alleviation in developing countries. Tourism is recognized as a highly climate‐sensitive sector, one that is also strongly influenced by environmental and socioeconomic change influenced by climate change, and is also a growing contributor to anthropogenic climate change. This article outlines the complex interrelationships between climate change and the multiple components of the international tourism system. Five focal themes that have developed within the literature on the consequences of climate change for tourism are then critically reviewed: climatic change and temporal and geographic shifts in tourism demand, climate‐induced environmental change and destination competitiveness within three major market segments (winter sports tourism, coastal tourism, and nature‐based tourism), and mitigation policy developments and future tourist mobility. The review highlights the differential vulnerability of tourism destinations and that the resultant changes in competitiveness and sustainability will transform some international tourism markets. Feedbacks throughout the tourism system mean that all destinations will need to adapt to the risks and opportunities posed by climate change and climate policy. While notable progress has been made in the last decade, a number of important knowledge gaps in each of the major impact areas, key regional knowledge gaps, and both tourist and tourism operator perceptions of climate change risks and adaptive capacity indicate that the tourism sector is not currently well prepared for the challenges of climate change. WIREs Clim Change 2012. doi: 10.1002/wcc.165 This article is categorized under: Climate and Development > Decoupling Emissions from Development
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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