Policies, methods and tools for visitor management proceedings of the second International Conference on Monitoring and Management of Visitor Flows in Recreational and Protected Areas, June 16 20, 2004, Rovaniemi, Finland
Bibliographic record
Abstract
The second International Conference on Monitoring and Management of Visitor Flows in recreational and protected areas (MMV 2) -conference provided a forum for research presentations and for exchange of information and experience of managerial policies, problems, practices and solutions regarding issues related to monitoring and management of visitor flows in recreational and protected areas. These proceedings cover ten research topics, which were chosen to reflect current on-going research work internationally in the field of visitor monitoring and management. Monitoring visitor flows and also other types of recreational inventories are discussed in 16 articles and four posters on visitor monitoring methods, experiences of national, regional and on-site visitor inventories and visitor flow modeling and data management. Nineteen papers and three posters are discussing visitor management research from several perspectives. Articles related to issues of visitor conflicts, implementation of visitor information in management processes, different aspects of sustainability and carrying capacity issues in recreational settings make the largest group of papers. The third major subject group of articles (16) deal with visitor management policy issues, and nature tourism policies in recreational and protected areas. The last topics include economic and social impacts of recreation and nature tourism in the surroundings communities, regions and countries.
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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 | 0.044 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".