Using internet search behavior to assess public awareness of protected wetlands
Bibliographic record
Abstract
Improving public awareness of protected wetlands facilitates sustainable wetland management, which depends on public participation. One way of gauging public interest is by tracking Internet search behavior (ISB). We assessed public awareness of issues related to protected wetland areas (PWAs) in South Korea by examining the frequencies of specific queries (PWAs, Ramsar, Upo wetland, Sunchon Bay, etc.) using relative search volumes (RSVs) obtained from an Internet search engine. RSV shows how many times a search term is used relative to a second search term during a specific period. Public awareness of PWAs changed from 2007 to 2013. Initially the majority of Internet searches were related to the most well-known tidal and inland wetlands Sunchon Bay and Upo wetlands, which are the largest existing wetlands in Korea with the greatest historical exposure. Public awareness, as reflected in RSVs, of wetlands increased significantly following PWA designation for the wetlands in 2008, which followed the Ramsar 10th Conference of Contracting Parties to the Convention on Wetlands (COP10) meeting. Public interest was strongly correlated to the number of news articles in the popular media, as evidenced by the increase in Internet searches for specific wetlands and words associated with specific wetlands. Correspondingly, the number of visitors to specific wetlands increased. To increase public interest in wetlands, wetland aspects that enhance wetland conservation should be promoted by the government and enhanced via public education. Our approach can be used to gauge public awareness and participation in a wide range of conservation efforts.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".