Impacts of tourism on coastal areas
Bibliographic record
Abstract
Abstract The socioeconomics of the Anthropocene is exposing coastal regions to multiple pressures, including climate change hazards, resource degradation, urban development and inequality. Tourism is often raised as either a panacea to, or exacerbator of, such threats to ecosystems and sustainable livelihoods. To better understand the impacts of tourism on coastal areas, Scopus and Web of Science databases were searched for the top 100 cited papers on coastal tourism. Web of Science suggested ‘highly cited’ papers were also included to allow for more recent high-impact papers. Of the papers retrieved, 44 focused on the impacts of tourism. Social/cultural and environmental impacts were viewed as mostly negative, while economic impacts were viewed as mostly positive but only of actual benefit to a few. In addition, when compared with recent whole-of-sector reviews and reports it was evident that coastal tourism is increasingly a global enterprise dominated by large corporations that leverage various interests across local to transnational scales. Through this global enterprise, even the positive economic benefits identified were overshadowed by a broader system of land and property development fuelling local wealth inequity and furthering the interests of offshore beneficiaries. Only two highly cited papers discussed tourism within a broader context of integrated coastal zone management, suggesting that tourism is mostly assessed as a discrete sector within the coastal zone and peripheral to other coastal management considerations or the global tourism sector as a whole. The findings have relevance to the holistic management of coasts, coastal tourism and the achievement of sustainable development goals in a way that considers the increasing threats from coastal hazards, resource extraction and urbanisation, as well as the pervasive impacts of international business systems from local to global scales.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".