Environmental Management Systems (EMS) of Tour Operators: Learning from Each Other
Bibliographic record
Abstract
This research determined to what extent tour operators in Western Canada who use natural, public-owned assets as a main feature of their business operations have developed formal or informal environmental management systems (EMS)and which EMS elements they use most frequently and most successfully. Furthermore, the research investigated which variables drive the use of EMS elements to ensure good environmental performance. The findings suggest that those operators who seek environmentally related business outcomes, possess supply-side tourism development values, and have more business experience, will have developed more sophisticated EMS and find these systems more useful in ensuring good environmental performance. With this information, park officials can develop a plan for knowledge sharing and the education of all tour operators. Park officials can design appropriate training and development programmes by considering the barriers that they might encounter in encouraging tour operators to implement certain policies and procedures. Tour operators can transfer some elements of EMS, widely recognised as useful, to other tour operators by ‘assimilation’, as few barriers exist to the implementation of these elements. However, if beliefs and attitudes must change before operators will use an element, learning by ‘accommodation’ will be a more appropriate approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".