The role of operators in sustainable whale-watching tourism: Proposing a continuous training framework
Bibliographic record
Abstract
Whale watching is considered a form of green tourism, but can affect marine ecosystems, impacting cetaceans' behavior and potentially increasing acoustic pollution. A more sustainable whale-watching practice should employ a comprehensive approach involving all stakeholders, but whale-watching operators are rarely involved. We propose a method to assess whale-watching operators' perceptions regarding the possible effects of their activity on marine fauna and preferred mitigation solutions, by means of online questionnaires and website communication strategies. Results from Canadian whale-watching operators show that they observe regulations regarding distance to whales but only partially perceive general vessels' impacts on fauna. Three recognized whale-watching experts identify the need for continuous training targeted at operators, which should include the impacts on marine ecosystems. A continuous training framework is proposed that targets whale-watching operators in addition to tourists, and involves scientists in several steps of the approach. This study serves as a starting point to involve operators' in order to advance towards a sustainable whale-watching tourism.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".