Bibliographic record
Abstract
This publication does not have an abstract. The introduction is displayed as the abstract. Wildlife-based tours in marine and coastal areas provide a range of psychological, educational and conservation benefits for visitors encountering marine animals (Higham, 1998; Drams, 2000; Schanzel and Mclntosh, 2000; Luck, 2003; Flnkler and Higham, 2004; Mayes et al.,2004; Hughes and Saunders, 2005; Tisdell and Wilson, 2005; Andersen and Miller, 2006). This chapter reviews and evaluates benefits for participants on marine wildlife tours. The focus is on non-consumptlve, free-ranging marine wildlife tourism where visitors can view, photograph, feed, and swim with, or assist in research on, marine animals in their natural habitats. Other broader participants include the marine tour operators, coastal and island communities in marine areas and researchers studying marine wildlife and/or tourists. Most research on marine wildlife tourism addresses environmental impacts on sea animals, industry compliance with codes of conduct and managing visitor interactions with marine species. However, this chapter reviews studies that primarily focus on tourist experiences of marine and coastal wildlife in Australia, New Zealand, Scotland and western Canada/USA. Much of this research on marine wildlife tourism is site or species specific and limited to one type of encounter. There is a need for more systematic, in-depth evaluation of marine wildlife tourism experiences and educational programmes to identify techniques that increase tourist benefits and knowledge, promoting attitude shifts and lifestyle changes (Samuels et al., 2003). In addition, both on-site and longer-term conservation behaviours that benefit marine wildlife and marine environments need to be explored. This chapter introduces marine wildlife tours and visitor benefits from marine wildlife encounters, then critically reviews the psychological, educational and conservation benefits of tourist participation in a range of marine wildlife experiences.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.117 | 0.028 |
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".