Best beaches of the world: a critique of web-based rating
Bibliographic record
Abstract
Abstract This study analyzes the content of internet ratings of beaches to identify the indicators used. The methodology used an exploratory internet survey using the term ‘best beaches’ in five languages. For each site, the ranking method used was extracted and the indicators considered were listed, where applicable. Of the 70 websites analyzed, 47 ranked the beaches (67%) but less than 50% used indicators. The remaining were based on the opinion of the editorial board, personal experience, and users’ perceptions. The most used indicator was the color of water, followed by the color of the sand. These results show that the majority of ‘best beaches’ lists are based on subjective criteria. They are an overview of places that appeal to the person that wrote the page and are not scientifically or analytically based. Even when indicators are considered, these are mostly a reflection of the idea of an idealized beach, crystal blue waters with white or gold sand. The actual quality of the beach, including water quality, carrying capacity, and ecosystem balance, is not addressed. Although visual attractiveness is a key element for the public, these rankings should incorporate a wider range of indicators to fully assess the quality of a beach.
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 | 0.112 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".